# # Mounting Google Drive
# from google.colab import drive
# drive.mount('/content/drive')

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Step 1:
# Import the excel data as dataframe using pandas
data_original = pd.read_excel("Data+Set+-+industrial_safety_and_health_database_with_accidents_description.xlsx")
# data_original = pd.read_excel('/content/drive/MyDrive/Great Learning/Capstone/Data+Set+-+industrial_safety_and_health_database_with_accidents_description.xlsx')
data_original.head()

print("There are {0} rows and {1} columns in the original Data Frame".format(data_original.shape[0], data_original.shape[1]))

There are 425 rows and 11 columns in the original Data Frame

# Checking datatypes and null values in the dataset columns
data_original.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 425 entries, 0 to 424
Data columns (total 11 columns):
 #   Column                    Non-Null Count  Dtype         
---  ------                    --------------  -----         
 0   Unnamed: 0                425 non-null    int64         
 1   Data                      425 non-null    datetime64[ns]
 2   Countries                 425 non-null    object        
 3   Local                     425 non-null    object        
 4   Industry Sector           425 non-null    object        
 5   Accident Level            425 non-null    object        
 6   Potential Accident Level  425 non-null    object        
 7   Genre                     425 non-null    object        
 8   Employee or Third Party   425 non-null    object        
 9   Critical Risk             425 non-null    object        
 10  Description               425 non-null    object        
dtypes: datetime64[ns](1), int64(1), object(9)
memory usage: 36.7+ KB

# checking for null values
data_original.isnull().sum()

Unnamed: 0                  0
Data                        0
Countries                   0
Local                       0
Industry Sector             0
Accident Level              0
Potential Accident Level    0
Genre                       0
Employee or Third Party     0
Critical Risk               0
Description                 0
dtype: int64

# checking for uniques values in each column
data_original.nunique()

Unnamed: 0                  425
Data                        287
Countries                     3
Local                        12
Industry Sector               3
Accident Level                5
Potential Accident Level      6
Genre                         2
Employee or Third Party       3
Critical Risk                33
Description                 411
dtype: int64

# Removing unnamed column
df = data_original.drop("Unnamed: 0", axis=1)
df.head()

# Renaming columns: 'Data', 'Countries', 'Local', 'Genre' and 'Employee or Third Party'
df.rename(columns={'Data':'Date', 'Countries':'Country', 'Local':'City', 'Genre':'Gender', 'Employee or Third Party':'Employee type'}, inplace=True)
df.head()

# Checking the duplicate records
df.duplicated().sum()

7

# Viewing the duplicate records
df[df.duplicated()]

# Removing the duplicate records
df.drop_duplicates(inplace=True)
df.shape

(418, 10)

# getting information on the data after some data cleaning activities
df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 418 entries, 0 to 424
Data columns (total 10 columns):
 #   Column                    Non-Null Count  Dtype         
---  ------                    --------------  -----         
 0   Date                      418 non-null    datetime64[ns]
 1   Country                   418 non-null    object        
 2   City                      418 non-null    object        
 3   Industry Sector           418 non-null    object        
 4   Accident Level            418 non-null    object        
 5   Potential Accident Level  418 non-null    object        
 6   Gender                    418 non-null    object        
 7   Employee type             418 non-null    object        
 8   Critical Risk             418 non-null    object        
 9   Description               418 non-null    object        
dtypes: datetime64[ns](1), object(9)
memory usage: 35.9+ KB

# getting more information about the cleaned data
df.describe(include = 'object')

# Calculate value counts and percentages for the 'Category' column
value_counts = df['Country'].value_counts()
percentages = df['Country'].value_counts(normalize=True) * 100

# Plot the bar chart
plt.figure(figsize=(8, 6))
value_counts.plot(kind='bar', color='skyblue')
plt.title('Value Counts and Percentages for Country Column')
plt.ylabel('Count')
plt.xlabel('Country')

# Show the counts and percentages on top of the bars
for i, (count, pct) in enumerate(zip(value_counts, percentages)):
    plt.text(i, count + 1, f"{count} ({pct:.2f}%)", ha='center', fontweight='bold')

plt.show()

# Calculate value counts and percentages for the 'City' column
value_counts = df['City'].value_counts()
percentages = df['City'].value_counts(normalize=True) * 100

# Plot the bar chart
plt.figure(figsize=(16, 6))
value_counts.plot(kind='bar', color='skyblue')
plt.title('Value Counts and Percentages for Country Column')
plt.ylabel('Count')
plt.xlabel('City')

# Show the counts and percentages on top of the bars
for i, (count, pct) in enumerate(zip(value_counts, percentages)):
    plt.text(i, count + 0.5, f"{count} ({pct:.2f}%)", ha='center', fontweight='bold')

plt.show()

# Calculate value counts and percentages for the 'Industry Sector' column
value_counts = df['Industry Sector'].value_counts()
percentages = df['Industry Sector'].value_counts(normalize=True) * 100

# Plot the bar chart
plt.figure(figsize=(10, 6))
value_counts.plot(kind='bar', color='skyblue')
plt.title('Value Counts and Percentages for Industry Sector Column')
plt.ylabel('Count')
plt.xlabel('Industry Sector')

# Show the counts and percentages on top of the bars
for i, (count, pct) in enumerate(zip(value_counts, percentages)):
    plt.text(i, count + 0.5, f"{count} ({pct:.2f}%)", ha='center', fontweight='bold')

plt.show()

# Calculate value counts and percentages for the 'Accident Level' column
value_counts = df['Accident Level'].value_counts()
percentages = df['Accident Level'].value_counts(normalize=True) * 100

# Plot the bar chart
plt.figure(figsize=(10, 6))
value_counts.plot(kind='bar', color='skyblue')
plt.title('Value Counts and Percentages for Accident Level Column')
plt.ylabel('Count')
plt.xlabel('Accident Level')

# Show the counts and percentages on top of the bars
for i, (count, pct) in enumerate(zip(value_counts, percentages)):
    plt.text(i, count + 0.5, f"{count} ({pct:.2f}%)", ha='center', fontweight='bold')

plt.show()

# Calculate value counts and percentages for the 'Potential Accident Level' column
value_counts = df['Potential Accident Level'].value_counts()
percentages = df['Potential Accident Level'].value_counts(normalize=True) * 100

# Plot the bar chart
plt.figure(figsize=(10, 6))
value_counts.plot(kind='bar', color='skyblue')
plt.title('Value Counts and Percentages for Potential Accident Level Column')
plt.ylabel('Count')
plt.xlabel('Potential Accident Level')

# Show the counts and percentages on top of the bars
for i, (count, pct) in enumerate(zip(value_counts, percentages)):
    plt.text(i, count + 0.5, f"{count} ({pct:.2f}%)", ha='center', fontweight='bold')

plt.show()

# Calculate value counts and percentages for the 'Gender' column
value_counts = df['Gender'].value_counts()
percentages = df['Gender'].value_counts(normalize=True) * 100

# Plot the bar chart
plt.figure(figsize=(10, 6))
value_counts.plot(kind='bar', color='skyblue')
plt.title('Value Counts and Percentages for Gender Column')
plt.ylabel('Count')
plt.xlabel('Gender')

# Show the counts and percentages on top of the bars
for i, (count, pct) in enumerate(zip(value_counts, percentages)):
    plt.text(i, count + 0.5, f"{count} ({pct:.2f}%)", ha='center', fontweight='bold')

plt.show()

# Calculate value counts and percentages for the 'Employee type' column
value_counts = df['Employee type'].value_counts()
percentages = df['Employee type'].value_counts(normalize=True) * 100

# Plot the bar chart
plt.figure(figsize=(10, 6))
value_counts.plot(kind='bar', color='skyblue')
plt.title('Value Counts and Percentages for Employee type Column')
plt.ylabel('Count')
plt.xlabel('Employee type')

# Show the counts and percentages on top of the bars
for i, (count, pct) in enumerate(zip(value_counts, percentages)):
    plt.text(i, count + 0.5, f"{count} ({pct:.2f}%)", ha='center', fontweight='bold')

plt.show()

# Calculate value counts for the 'Critical Risk' column
value_counts = df['Critical Risk'].value_counts()

# Plot the bar chart
plt.figure(figsize=(15, 6))
value_counts.plot(kind='bar', color='skyblue')
plt.title('Value Counts for Critical Risk Column')
plt.ylabel('Count')
plt.xlabel('Critical Risk')

# Show the percentages on top of the bars
for i, v in enumerate(value_counts):
    plt.text(i, v + 0.5, f"{v:.0f}", size = 8, ha='center', fontweight='bold')

plt.show()

df2 = df.copy()

# Extract Year, Month, Day, Weekday, Week of the Year
df2['Date'] = pd.to_datetime(df2['Date'])
df2['Year'] = df2['Date'].dt.year
df2['Month'] = df2['Date'].dt.month
df2['Day'] = df2['Date'].dt.day
df2['Weekday'] = df2['Date'].dt.weekday  # Monday=0, Sunday=6
df2['Week of the Year'] = df2['Date'].dt.isocalendar().week  # ISO week of the year--------Need clarity to deal with 53rd week in january
## https://stackoverflow.com/questions/35749982/show-week-53-as-yearweek-in-python

# Function to determine the season based on Brazil's climatology
def get_season_brazil(month):
    if month in [12, 1, 2]:
        return 'Summer'
    elif month in [3, 4, 5]:
        return 'Autumn'
    elif month in [6, 7, 8]:
        return 'Winter'
    elif month in [9, 10, 11]:
        return 'Spring'

# Apply the function to create the 'Season' column based on Brazil's climatology
df2['Season'] = df2['Month'].apply(get_season_brazil)

# Display the updated DataFrame
df2.head()

df2.tail()

df3 = df2.copy()

# Add a 'count' column to represent the occurrence of an incident
df3['count'] = 1  # Assuming each row represents one incident

# Set the 'Date' column as the DataFrame index
df3.set_index('Date', inplace=True)

# Resample data by day, week, month, and year to count incidents
daily_data = df3.resample('D').sum()  # Resampling by day
weekly_data = df3.resample('W').sum()  # Resampling by week
monthly_data = df3.resample('M').sum()  # Resampling by month
yearly_data = df3.resample('Y').sum()  # Resampling by year

# Plotting
plt.figure(figsize=(14, 10))

# Daily incidents line chart
plt.subplot(2, 2, 1)
plt.plot(daily_data.index, daily_data['count'], marker='o', linestyle='-')
plt.title('Incidents per Day')
plt.xlabel('Date')
plt.ylabel('Number of Incidents')

# Weekly incidents line chart
plt.subplot(2, 2, 2)
plt.plot(weekly_data.index, weekly_data['count'], marker='o', linestyle='-')
plt.title('Incidents per Week')
plt.xlabel('Date')
plt.ylabel('Number of Incidents')

# Monthly incidents line chart
plt.subplot(2, 2, 3)
plt.plot(monthly_data.index, monthly_data['count'], marker='o', linestyle='-')
plt.title('Incidents per Month')
plt.xlabel('Date')
plt.ylabel('Number of Incidents')

# Yearly incidents line chart
plt.subplot(2, 2, 4)
plt.plot(yearly_data.index, yearly_data['count'], marker='o', linestyle='-')
plt.title('Incidents per Year')
plt.xlabel('Date')
plt.ylabel('Number of Incidents')

# Adjust layout and show the plots
plt.tight_layout()
plt.show()

C:\Users\anime\AppData\Local\Temp\ipykernel_21540\4128195198.py:10: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  monthly_data = df3.resample('M').sum()  # Resampling by month
C:\Users\anime\AppData\Local\Temp\ipykernel_21540\4128195198.py:11: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
  yearly_data = df3.resample('Y').sum()  # Resampling by year

# Creating the above charts as interactive charts to get more detailed analysis
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px

df3 = df2.copy()

# Convert the 'Date' column to datetime format
df3['Date'] = pd.to_datetime(df3['Date'])

# Add a 'count' column to represent the occurrence of an incident
df3['count'] = 1  # Assuming each row represents one incident

# Set the 'Date' column as the DataFrame index
df3.set_index('Date', inplace=True)

# Resample data by day, week, month, and year to count incidents
daily_data = df3.resample('D').sum()  # Resampling by day
weekly_data = df3.resample('W').sum()  # Resampling by week
monthly_data = df3.resample('M').sum()  # Resampling by month
yearly_data = df3.resample('Y').sum()  # Resampling by year

# Apply the function to create the 'Season' column based on Brazil's climatology
df3['Month'] = df3.index.month
df3['Season'] = df3['Month'].apply(get_season_brazil)

# Group by season to count incidents
season_data = df3.groupby('Season')['count'].sum().reindex(['Summer', 'Autumn', 'Winter', 'Spring'])

# Create individual charts for each time period

# Daily incidents line chart
fig_daily = go.Figure()
fig_daily.add_trace(go.Scatter(x=daily_data.index, y=daily_data['count'], mode='lines+markers', name='Daily'))
fig_daily.update_layout(
    title='Incidents per Day',
    xaxis_title='Date',
    yaxis_title='Number of Incidents',
    hovermode='x unified'
)
fig_daily.show()

# Weekly incidents line chart
fig_weekly = go.Figure()
fig_weekly.add_trace(go.Scatter(x=weekly_data.index, y=weekly_data['count'], mode='lines+markers', name='Weekly'))
fig_weekly.update_layout(
    title='Incidents per Week',
    xaxis_title='Date',
    yaxis_title='Number of Incidents',
    hovermode='x unified'
)
fig_weekly.show()

# Monthly incidents line chart
fig_monthly = go.Figure()
fig_monthly.add_trace(go.Scatter(x=monthly_data.index, y=monthly_data['count'], mode='lines+markers', name='Monthly'))
fig_monthly.update_layout(
    title='Incidents per Month',
    xaxis_title='Date',
    yaxis_title='Number of Incidents',
    hovermode='x unified'
)
fig_monthly.show()

# Yearly incidents line chart
fig_yearly = go.Figure()
fig_yearly.add_trace(go.Scatter(x=yearly_data.index, y=yearly_data['count'], mode='lines+markers', name='Yearly'))
fig_yearly.update_layout(
    title='Incidents per Year',
    xaxis_title='Date',
    yaxis_title='Number of Incidents',
    hovermode='x unified'
)
fig_yearly.show()

C:\Users\anime\AppData\Local\Temp\ipykernel_21540\174771619.py:13: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  monthly_data = df3.resample('M').sum()  # Resampling by month
C:\Users\anime\AppData\Local\Temp\ipykernel_21540\174771619.py:14: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
  yearly_data = df3.resample('Y').sum()  # Resampling by year

df3 = df2.copy()

# Creating trend chart on number of incidents as per the seasons.
# Convert the 'Date' column to datetime format
df3['Date'] = pd.to_datetime(df3['Date'])

# Add a 'count' column to represent the occurrence of an incident
df3['count'] = 1  # Assuming each row represents one incident

# Set the 'Date' column as the DataFrame index
df3.set_index('Date', inplace=True)

# Function to determine the season based on Brazil's climatology
def get_season_brazil(month):
    if month in [12, 1, 2]:
        return 'Summer'
    elif month in [3, 4, 5]:
        return 'Autumn'
    elif month in [6, 7, 8]:
        return 'Winter'
    elif month in [9, 10, 11]:
        return 'Spring'

# Extract Year and Month, and create a 'Season' column
df3['Year'] = df3.index.year
df3['Month'] = df3.index.month
df3['Season'] = df3['Month'].apply(get_season_brazil)

# Group by both Year and Season, summing the incident counts
season_data_by_year = df3.groupby(['Year', 'Season'])['count'].sum().reset_index()

# Ensure that the Season order is preserved as Summer, Autumn, Winter, Spring
season_order = ['Summer', 'Autumn', 'Winter', 'Spring']
season_data_by_year['Season'] = pd.Categorical(season_data_by_year['Season'], categories=season_order, ordered=True)

# Create a bar chart showing incidents by season and year
fig_season_year = px.bar(
    season_data_by_year,
    x='Season',
    y='count',
    color='Season',
    barmode='group',
    facet_col='Year',  # This will create a separate plot for each year
    title='Incidents by Season and Year (Brazil Climatology)',
    text='count',
    color_discrete_map={
        'Summer': 'orange', 'Autumn': 'brown', 'Winter': 'blue', 'Spring': 'green'
    }
)

# Update the layout of the seasonal chart
fig_season_year.update_layout(
    xaxis_title='Season',
    yaxis_title='Number of Incidents',
    hovermode='x'
)

# Show the updated seasonal chart
fig_season_year.show()

# Reviewing the data
df3.head(2)

# Regaining the index back
df3.reset_index(inplace = True)
df3.head()

# Accident level by country
plt.figure(figsize=(10, 6))
sns.countplot(x='Accident Level', hue='Country', data=df3)
plt.title('Accident Level by Country')
plt.show()

# Accident level by critical risk

# Optional: Filter the dataset to show only the top N critical risks (if applicable)
top_critical_risks = df3['Critical Risk'].value_counts().nlargest(10).index
filtered_df = df3[df3['Critical Risk'].isin(top_critical_risks)]

# Accident level by critical risk with adjusted figure size and bar width
plt.figure(figsize=(16, 10))  # Increased figure size
sns.countplot(x='Accident Level', hue='Critical Risk', data=filtered_df, palette='Set2', width=0.8)
plt.title('Accident Level Distribution by Critical Risk (Top 10)', fontsize=18)
plt.xlabel('Accident Level', fontsize=16)
plt.ylabel('Count', fontsize=16)
plt.xticks(rotation=45, fontsize=14)  # Adjusted rotation and font size
plt.legend(title='Critical Risk', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=12)
plt.tight_layout()  # Adjust layout to fit everything
plt.show()

# Accident Severity Analysis (Accident Level vs Potential Accident Level): This is to analyze if the accidents are mostly minor ( Level 1-II) or
# are severe accidents ( V-VI)

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd # Import pandas for data manipulation

# Create a contingency table
contingency_table = pd.crosstab(df3['Accident Level'], df3['Potential Accident Level'])

# Reset index to convert the multi-index to columns
contingency_table = contingency_table.reset_index()

# Melt the table to create a 'Count' column for the heatmap
comparison = contingency_table.melt(id_vars=['Accident Level'], var_name='Potential Accident Level', value_name='Count')


# Pivot the data for a heatmap
# Use 'comparison' which now contains the necessary data
comparison_pivot = comparison.pivot(index="Accident Level", columns="Potential Accident Level", values="Count")

# Plot the heatmap
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
sns.heatmap(comparison_pivot, annot=True, cmap="YlGnBu", fmt='g')
plt.title('Actual Accident Level vs. Potential Accident Level')
plt.xlabel('Potential Accident Level')
plt.ylabel('Accident Level')
plt.show()

# Violin Plot to show the distribution
plt.figure(figsize=(10, 6))
sns.violinplot(data=df3, x='Accident Level', y='Potential Accident Level')
plt.title('Violin Plot of Potential Accident Level by Actual Accident Level')
plt.xlabel('Actual Accident Level')
plt.ylabel('Potential Accident Level')
plt.xticks(rotation=45)
plt.show()

# 1. High-Risk Sectors or Regions:
# Insight to derive: Which countries or industry sectors have higher accident rates and
# which ones frequently encounter severe accidents.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Group data by Countries and Industry Sector
country_sector_group = df3.groupby(['Country', 'Industry Sector']).size().reset_index(name='Accident Count')

# Display grouped data
print(country_sector_group.head())

      Country Industry Sector  Accident Count
0  Country_01          Metals              46
1  Country_01          Mining             200
2  Country_01          Others               2
3  Country_02          Metals              88
4  Country_02          Mining              37

# Visualize Accident Count by Countries and Industry Sectors

# Pivot the data for a heatmap
pivot_data = country_sector_group.pivot(index="Country", columns="Industry Sector", values="Accident Count") # Changed to keyword arguments

# Plot a heatmap of accident counts across countries and industry sectors
plt.figure(figsize=(10,6))
sns.heatmap(pivot_data, annot=True, cmap="Blues")
plt.title('Accident Count by Countries and Industry Sector')
plt.show()

# Analyze Accident Severity Distribution

# Group data by Countries, Industry sector, and Accident level
severity_group = df3.groupby(['Country', 'Industry Sector', 'Accident Level']).size().reset_index(name='Count')

# Pivot the data for plotting
severity_pivot = severity_group.pivot_table(index=['Country', 'Industry Sector'], columns='Accident Level', values='Count', fill_value=0)

# Plot stacked bar chart for accident severity
severity_pivot.plot(kind='bar', stacked=True, figsize=(12, 7), colormap='coolwarm')
plt.title('Accident Severity Distribution by Countries and Industry Sector')
plt.ylabel('Number of Accidents')
plt.show()

# Identify High-Risk Plants to analyze specific plants where the accident severity is consistently higher

# Group data by Countries, Local (plant), and Accident level
plant_risk_group = df3.groupby(['Country', 'City', 'Accident Level']).size().reset_index(name='Count')

# Find plants with high accident severity (Accident level >= IV)
high_risk_plants = plant_risk_group[plant_risk_group['Accident Level'] >= 'IV']

# # Display high-risk plants
# print(high_risk_plants)

import pandas as pd
from collections import Counter
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import string
import nltk

# Make sure to download NLTK resources if not already installed
nltk.download('punkt')
nltk.download('stopwords')

# Load the data (assuming the dataset is already loaded as 'data')
# Extract the 'Description' column
descriptions = df3['Description'].dropna()

# Preprocess the text
def preprocess_text(text):
    # Tokenize text
    tokens = word_tokenize(text.lower())

    # Remove punctuation
    tokens = [word for word in tokens if word.isalpha()]

    # Remove stopwords
    stop_words = set(stopwords.words('english'))
    tokens = [word for word in tokens if word not in stop_words]

    return tokens

# Apply preprocessing to all descriptions
all_words = []
for description in descriptions:
    tokens = preprocess_text(description)
    all_words.extend(tokens)

# Get the frequency of each word
word_freq = Counter(all_words)

# Convert the word frequencies to a DataFrame for better readability
word_freq_df = pd.DataFrame(word_freq.most_common(), columns=['Word', 'Frequency'])

# Display the top 20 most frequent words
print(word_freq_df.head(20))

[nltk_data] Downloading package punkt to
[nltk_data]     C:\Users\anime\AppData\Roaming\nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\anime\AppData\Roaming\nltk_data...
[nltk_data]   Package stopwords is already up-to-date!

            Word  Frequency
0        causing        166
1           hand        163
2       employee        156
3           left        155
4          right        154
5       operator        126
6         injury        104
7           time        101
8       activity         91
9           area         80
10        moment         78
11     equipment         76
12          work         76
13      accident         73
14  collaborator         71
15         level         70
16     assistant         68
17        finger         68
18        worker         67
19          pipe         67

!pip install wordcloud

Requirement already satisfied: wordcloud in c:\users\anime\anaconda3\lib\site-packages (1.9.3)
Requirement already satisfied: numpy>=1.6.1 in c:\users\anime\anaconda3\lib\site-packages (from wordcloud) (1.26.4)
Requirement already satisfied: pillow in c:\users\anime\anaconda3\lib\site-packages (from wordcloud) (10.3.0)
Requirement already satisfied: matplotlib in c:\users\anime\appdata\roaming\python\python311\site-packages (from wordcloud) (3.7.3)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\anime\anaconda3\lib\site-packages (from matplotlib->wordcloud) (1.2.0)
Requirement already satisfied: cycler>=0.10 in c:\users\anime\anaconda3\lib\site-packages (from matplotlib->wordcloud) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\anime\anaconda3\lib\site-packages (from matplotlib->wordcloud) (4.51.0)
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from wordcloud import WordCloud

# Combine all descriptions into one text
all_descriptions = " ".join(df3['Description'].dropna())

# Create the word cloud
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(all_descriptions)

# Display the word cloud
plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

df3.head()

# Displaying the complete text of the first five descriptions in the dataset
df.head()['Description'].values

array(['While removing the drill rod of the Jumbo 08 for maintenance, the supervisor proceeds to loosen the support of the intermediate centralizer to facilitate the removal, seeing this the mechanic supports one end on the drill of the equipment to pull with both hands the bar and accelerate the removal from this, at this moment the bar slides from its point of support and tightens the fingers of the mechanic between the drilling bar and the beam of the jumbo.',
       'During the activation of a sodium sulphide pump, the piping was uncoupled and the sulfide solution was designed in the area to reach the maid. Immediately she made use of the emergency shower and was directed to the ambulatory doctor and later to the hospital. Note: of sulphide solution = 48 grams / liter.',
       'In the sub-station MILPO located at level +170 when the collaborator was doing the excavation work with a pick (hand tool), hitting a rock with the flat part of the beak, it bounces off hitting the steel tip of the safety shoe and then the metatarsal area of \u200b\u200bthe left foot of the collaborator causing the injury.',
       'Being 9:45 am. approximately in the Nv. 1880 CX-695 OB7, the personnel begins the task of unlocking the Soquet bolts of the BHB machine, when they were in the penultimate bolt they identified that the hexagonal head was worn, proceeding Mr. Cristóbal - Auxiliary assistant to climb to the platform to exert pressure with your hand on the "DADO" key, to prevent it from coming out of the bolt; in those moments two collaborators rotate with the lever in anti-clockwise direction, leaving the key of the bolt, hitting the palm of the left hand, causing the injury.',
       'Approximately at 11:45 a.m. in circumstances that the mechanics Anthony (group leader), Eduardo and Eric Fernández-injured-the three of the Company IMPROMEC, performed the removal of the pulley of the motor of the pump 3015 in the ZAF of Marcy. 27 cm / Length: 33 cm / Weight: 70 kg), as it was locked proceed to heating the pulley to loosen it, it comes out and falls from a distance of 1.06 meters high and hits the instep of the right foot of the worker, causing the injury described.'],
      dtype=object)

# Displaying the complete text of the last five descriptions in the dataset
df.tail()['Description'].values

array(['Being approximately 5:00 a.m. approximately, when lifting the Kelly HQ towards the pulley of the frame to align it, the assistant Marco that is in the later one is struck the hand against the frame generating the injury.',
       'The collaborator moved from the infrastructure office (Julio to the toilets, when the pin of the right shoe is hooked on the bra of the left shoe causing not to take the step and fall untimely, causing injury described.',
       'During the environmental monitoring activity in the area, the employee was surprised by a swarming swarm of weevils. During the exit of the place, endured suffering two stings, being one in the face and the other in the middle finger of the left hand.',
       'The Employee performed the activity of stripping cathodes, when pulling the cathode sheet his hand hit the side of another cathode, causing a blunt cut on his 2nd finger of the left hand.',
       'At 10:00 a.m., when the assistant cleaned the floor of module "E" in the central camp, she slipped back and immediately grabbed the laundry table to avoid falling to the floor; suffering the described injury.'],
      dtype=object)

import re
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer

# Download necessary resources
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')

[nltk_data] Downloading package punkt to
[nltk_data]     C:\Users\anime\AppData\Roaming\nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\anime\AppData\Roaming\nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package wordnet to
[nltk_data]     C:\Users\anime\AppData\Roaming\nltk_data...
[nltk_data]   Package wordnet is already up-to-date!

True

# Initialize stopwords and lemmatizer
stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()

def preprocess_description(text):
    """
    Preprocess the text by performing NLP pre-processing steps.
    """
    # 1. Lowercasing
    text = text.lower()

    # 2. Remove punctuation and special characters
    text = re.sub(r'[^\w\s]', '', text)

    # 3. Tokenize the text
    tokens = word_tokenize(text)

    # 4. Remove stopwords
    tokens = [word for word in tokens if word not in stop_words]

    # 5. Lemmatize the tokens (optional: use stemming instead if preferred)
    tokens = [lemmatizer.lemmatize(word) for word in tokens]

    # 6. Remove numbers
    tokens = [word for word in tokens if not word.isdigit()]

    # Join tokens back into a string
    return ' '.join(tokens)

# Apply pre-processing to the "Description" column
df3['Preprocessed_Description'] = df3['Description'].apply(preprocess_description)

# Display the preprocessed descriptions
df3[['Description', 'Preprocessed_Description']].head()

df3.head()[['Description','Preprocessed_Description']].values

array([['While removing the drill rod of the Jumbo 08 for maintenance, the supervisor proceeds to loosen the support of the intermediate centralizer to facilitate the removal, seeing this the mechanic supports one end on the drill of the equipment to pull with both hands the bar and accelerate the removal from this, at this moment the bar slides from its point of support and tightens the fingers of the mechanic between the drilling bar and the beam of the jumbo.',
        'removing drill rod jumbo maintenance supervisor proceeds loosen support intermediate centralizer facilitate removal seeing mechanic support one end drill equipment pull hand bar accelerate removal moment bar slide point support tightens finger mechanic drilling bar beam jumbo'],
       ['During the activation of a sodium sulphide pump, the piping was uncoupled and the sulfide solution was designed in the area to reach the maid. Immediately she made use of the emergency shower and was directed to the ambulatory doctor and later to the hospital. Note: of sulphide solution = 48 grams / liter.',
        'activation sodium sulphide pump piping uncoupled sulfide solution designed area reach maid immediately made use emergency shower directed ambulatory doctor later hospital note sulphide solution gram liter'],
       ['In the sub-station MILPO located at level +170 when the collaborator was doing the excavation work with a pick (hand tool), hitting a rock with the flat part of the beak, it bounces off hitting the steel tip of the safety shoe and then the metatarsal area of \u200b\u200bthe left foot of the collaborator causing the injury.',
        'substation milpo located level collaborator excavation work pick hand tool hitting rock flat part beak bounce hitting steel tip safety shoe metatarsal area left foot collaborator causing injury'],
       ['Being 9:45 am. approximately in the Nv. 1880 CX-695 OB7, the personnel begins the task of unlocking the Soquet bolts of the BHB machine, when they were in the penultimate bolt they identified that the hexagonal head was worn, proceeding Mr. Cristóbal - Auxiliary assistant to climb to the platform to exert pressure with your hand on the "DADO" key, to prevent it from coming out of the bolt; in those moments two collaborators rotate with the lever in anti-clockwise direction, leaving the key of the bolt, hitting the palm of the left hand, causing the injury.',
        'approximately nv cx695 ob7 personnel begin task unlocking soquet bolt bhb machine penultimate bolt identified hexagonal head worn proceeding mr cristóbal auxiliary assistant climb platform exert pressure hand dado key prevent coming bolt moment two collaborator rotate lever anticlockwise direction leaving key bolt hitting palm left hand causing injury'],
       ['Approximately at 11:45 a.m. in circumstances that the mechanics Anthony (group leader), Eduardo and Eric Fernández-injured-the three of the Company IMPROMEC, performed the removal of the pulley of the motor of the pump 3015 in the ZAF of Marcy. 27 cm / Length: 33 cm / Weight: 70 kg), as it was locked proceed to heating the pulley to loosen it, it comes out and falls from a distance of 1.06 meters high and hits the instep of the right foot of the worker, causing the injury described.',
        'approximately circumstance mechanic anthony group leader eduardo eric fernándezinjuredthe three company impromec performed removal pulley motor pump zaf marcy cm length cm weight kg locked proceed heating pulley loosen come fall distance meter high hit instep right foot worker causing injury described']],
      dtype=object)

# Save the cleaned dataset as a .csv or .xlsx file
df3.to_csv('cleaned_industrial_safety_data.csv', index=False)

# Reading back the cleaned csv data created in previous steps as Dataframe
df4 = pd.read_csv('cleaned_industrial_safety_data.csv')
df4.head()

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Load and pre-process data (assuming pre-processing has been done as per previous code)
# We will use 'Preprocessed_Description' for features and 'Accident Level' as the target.

# Splitting the dataset into features and target
X = df4['Preprocessed_Description']  # Features (preprocessed descriptions)
y = df4['Accident Level']  # Target (accident levels)

# 1. TF-IDF Vectorization of the pre-processed descriptions
tfidf_vectorizer = TfidfVectorizer(max_features=1000)  # Limit to top 1000 features for simplicity
X_tfidf = tfidf_vectorizer.fit_transform(X)

# 2. Split the data into training and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X_tfidf, y, test_size=0.2, random_state=42, stratify=y)

# 3. Train various classification models
models = {
    "Naive_Bayes": MultinomialNB(),
    "Logistic_Regression": LogisticRegression(random_state=1),
    "SVM": SVC(random_state=1),
    "Random_Forest": RandomForestClassifier(random_state=1),
    "Gradient_Boosting": GradientBoostingClassifier(random_state=1)
}

# Initialize an empty list to store classification metrics
metrics_list = []

for model_name, model in models.items():
    # Train the model
    model.fit(X_train, y_train)

    # Make predictions on the test data
    y_pred = model.predict(X_test)

    # Get the classification report as a string
    report_str = classification_report(y_test, y_pred, zero_division=0)

    # Get the classification report as a dictionary
    report_dict = classification_report(y_test, y_pred, output_dict=True, zero_division=0)

    # Extract accuracy, precision, recall, and F1-score (average metrics for each model)
    accuracy = accuracy_score(y_test, y_pred)
    precision = report_dict['weighted avg']['precision']
    recall = report_dict['weighted avg']['recall']
    f1_score = report_dict['weighted avg']['f1-score']

    # Append the metrics to the list
    metrics_list.append({
        'Model': model_name,
        'Accuracy': accuracy,
        'Precision': precision,
        'Recall': recall,
        'F1-Score': f1_score
    })

    # Print the classification report for the current model
    print(f"Classification Report for {model_name}:")
    print(report_str)
    print("=" * 60)  # Divider for clarity

# Convert the list of metrics into a DataFrame for comparison
metrics_comparison = pd.DataFrame(metrics_list)

# Display the comparison of metrics for each model
print("Comparison of Metrics:")
print(metrics_comparison)

# Optional: Save the comparison to a CSV for later use
# metrics_comparison.to_csv('/mnt/data/model_metrics_comparison.csv', index=False)

Classification Report for Naive_Bayes:
              precision    recall  f1-score   support

           I       0.74      1.00      0.85        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

============================================================
Classification Report for Logistic_Regression:
              precision    recall  f1-score   support

           I       0.74      1.00      0.85        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

============================================================
Classification Report for SVM:
              precision    recall  f1-score   support

           I       0.74      1.00      0.85        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

============================================================
Classification Report for Random_Forest:
              precision    recall  f1-score   support

           I       0.76      1.00      0.86        62
          II       0.50      0.12      0.20         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.75        84
   macro avg       0.25      0.23      0.21        84
weighted avg       0.61      0.75      0.65        84

============================================================
Classification Report for Gradient_Boosting:
              precision    recall  f1-score   support

           I       0.75      0.92      0.83        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.25      0.17      0.20         6
           V       0.00      0.00      0.00         2

    accuracy                           0.69        84
   macro avg       0.20      0.22      0.21        84
weighted avg       0.57      0.69      0.62        84

============================================================
Comparison of Metrics:
                 Model  Accuracy  Precision    Recall  F1-Score
0          Naive_Bayes  0.738095   0.544785  0.738095  0.626875
1  Logistic_Regression  0.738095   0.544785  0.738095  0.626875
2                  SVM  0.738095   0.544785  0.738095  0.626875
3        Random_Forest  0.750000   0.605691  0.750000  0.654630
4    Gradient_Boosting  0.690476   0.571429  0.690476  0.624017

# Load and pre-process data (assuming pre-processing has been done as per previous code)
# We will use 'Preprocessed_Description' for features and 'Accident Level' as the target.

# Splitting the dataset into features and target
X = df4['Preprocessed_Description']  # Features (preprocessed descriptions)
y = df4['Potential Accident Level']  # Target (Potential accident levels)

# 1. TF-IDF Vectorization of the pre-processed descriptions
tfidf_vectorizer = TfidfVectorizer(max_features=1000)  # Limit to top 1000 features for simplicity
X_tfidf = tfidf_vectorizer.fit_transform(X)

# 2. Split the data into training and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X_tfidf, y, test_size=0.2, random_state=42) #, stratify=y)

# 3. Train various classification models
models = {
    "Naive_Bayes": MultinomialNB(),
    "Logistic_Regression": LogisticRegression(random_state=1),
    "SVM": SVC(random_state=1),
    "Random_Forest": RandomForestClassifier(random_state=1),
    "Gradient_Boosting": GradientBoostingClassifier(random_state=1)
}

# Initialize an empty list to store classification metrics
metrics_list = []

for model_name, model in models.items():
    # Train the model
    model.fit(X_train, y_train)

    # Make predictions on the test data
    y_pred = model.predict(X_test)

    # Get the classification report as a string
    report_str = classification_report(y_test, y_pred, zero_division=0)

    # Get the classification report as a dictionary
    report_dict = classification_report(y_test, y_pred, output_dict=True, zero_division=0)

    # Extract accuracy, precision, recall, and F1-score (average metrics for each model)
    accuracy = accuracy_score(y_test, y_pred)
    precision = report_dict['weighted avg']['precision']
    recall = report_dict['weighted avg']['recall']
    f1_score = report_dict['weighted avg']['f1-score']

    # Append the metrics to the list
    metrics_list.append({
        'Model': model_name,
        'Accuracy': accuracy,
        'Precision': precision,
        'Recall': recall,
        'F1-Score': f1_score
    })

    # Print the classification report for the current model
    print(f"Classification Report for {model_name}:")
    print(report_str)
    print("=" * 60)  # Divider for clarity

# Convert the list of metrics into a DataFrame for comparison
metrics_comparison = pd.DataFrame(metrics_list)

# Display the comparison of metrics for each model
print("Comparison of Metrics:")
print(metrics_comparison)

# Optional: Save the comparison to a CSV for later use
# metrics_comparison.to_csv('/mnt/data/model_metrics_comparison.csv', index=False)

Classification Report for Naive_Bayes:
              precision    recall  f1-score   support

           I       1.00      0.33      0.50         9
          II       0.67      0.35      0.46        17
         III       0.40      0.07      0.12        27
          IV       0.36      0.92      0.52        26
           V       0.00      0.00      0.00         5

    accuracy                           0.42        84
   macro avg       0.48      0.34      0.32        84
weighted avg       0.48      0.42      0.35        84

============================================================
Classification Report for Logistic_Regression:
              precision    recall  f1-score   support

           I       1.00      0.33      0.50         9
          II       0.67      0.47      0.55        17
         III       0.20      0.04      0.06        27
          IV       0.36      0.88      0.51        26
           V       0.00      0.00      0.00         5

    accuracy                           0.42        84
   macro avg       0.45      0.35      0.33        84
weighted avg       0.42      0.42      0.34        84

============================================================
Classification Report for SVM:
              precision    recall  f1-score   support

           I       1.00      0.22      0.36         9
          II       1.00      0.18      0.30        17
         III       0.67      0.07      0.13        27
          IV       0.34      1.00      0.51        26
           V       0.00      0.00      0.00         5

    accuracy                           0.39        84
   macro avg       0.60      0.29      0.26        84
weighted avg       0.63      0.39      0.30        84

============================================================
Classification Report for Random_Forest:
              precision    recall  f1-score   support

           I       1.00      0.33      0.50         9
          II       0.50      0.24      0.32        17
         III       0.45      0.19      0.26        27
          IV       0.39      0.92      0.55        26
           V       0.00      0.00      0.00         5

    accuracy                           0.43        84
   macro avg       0.47      0.34      0.33        84
weighted avg       0.47      0.43      0.37        84

============================================================
Classification Report for Gradient_Boosting:
              precision    recall  f1-score   support

           I       1.00      0.33      0.50         9
          II       0.62      0.47      0.53        17
         III       0.50      0.22      0.31        27
          IV       0.40      0.77      0.53        26
           V       0.20      0.20      0.20         5
          VI       0.00      0.00      0.00         0

    accuracy                           0.45        84
   macro avg       0.45      0.33      0.34        84
weighted avg       0.53      0.45      0.44        84

============================================================
Comparison of Metrics:
                 Model  Accuracy  Precision    Recall  F1-Score
0          Naive_Bayes  0.416667   0.481509  0.416667  0.346911
1  Logistic_Regression  0.416667   0.417584  0.416667  0.343520
2                  SVM  0.392857   0.629699  0.392857  0.300329
3        Random_Forest  0.428571   0.474253  0.428571  0.371751
4    Gradient_Boosting  0.452381   0.528114  0.452381  0.435221

from imblearn.over_sampling import SMOTE

# Load and pre-process data (assuming pre-processing has been done as per previous code)
# We will use 'Preprocessed_Description' for features and 'Accident Level' as the target.

# Splitting the dataset into features and target
X = df4['Preprocessed_Description']  # Features (preprocessed descriptions)
y = df4['Accident Level']  # Target (accident levels)

# 1. TF-IDF Vectorization of the pre-processed descriptions
tfidf_vectorizer = TfidfVectorizer(max_features=1000)  # Limit to top 1000 features for simplicity
X_tfidf = tfidf_vectorizer.fit_transform(X)

# 2. Split the data into training and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X_tfidf, y, test_size=0.2, random_state=42, stratify=y)

# 3. Apply SMOTE to the training data
smote = SMOTE(random_state=42)
X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)

# checking if the training data is balanced
y_train_smote.value_counts()

Accident Level
I      247
II     247
III    247
IV     247
V      247
Name: count, dtype: int64

# Naive Base Model on original training data
Naive_Bayes = MultinomialNB()
Naive_Bayes.fit(X_train, y_train)
# Checking train accuracy
model_score_train = Naive_Bayes.score(X_train, y_train)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = Naive_Bayes.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.7395209580838323
Test Accuracy: 0.7380952380952381

from sklearn import metrics

#predict on test
y_predict = Naive_Bayes.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.74      1.00      0.85        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# Naive bayes Base Model on balanced training data
Naive_Bayes_smote = MultinomialNB()
Naive_Bayes_smote.fit(X_train_smote, y_train_smote)
# Checking train accuracy
model_score_train = Naive_Bayes_smote.score(X_train_smote, y_train_smote)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = Naive_Bayes_smote.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.9603238866396762
Test Accuracy: 0.6309523809523809

#predict on test
y_predict = Naive_Bayes_smote.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.86      0.68      0.76        62
          II       0.22      0.50      0.31         8
         III       0.44      0.67      0.53         6
          IV       0.40      0.33      0.36         6
           V       0.33      0.50      0.40         2

    accuracy                           0.63        84
   macro avg       0.45      0.54      0.47        84
weighted avg       0.72      0.63      0.66        84

from sklearn.model_selection import GridSearchCV

# grid search
# Choose the type of classifier.
Naive_Bayes_tuned = MultinomialNB()

# Grid of parameters to choose from
parameters = {'alpha': [0.1, 0.5, 1.0, 2.0],  # Smoothing parameter
    'fit_prior': [True, False]
             }

# Type of scoring used to compare parameter combinations
# acc_scorer = metrics.make_scorer(metrics.recall_score)


# Run the grid search
grid_obj = GridSearchCV(Naive_Bayes_tuned, parameters,cv=5) # , scoring=acc_scorer - # by default the scoring metrics would be the accuracy
# Used 5-fold cross validation
grid_obj = grid_obj.fit(X_train_smote, y_train_smote)

# Set the model to the best combination of parameters
Naive_Bayes_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
Naive_Bayes_tuned.fit(X_train_smote, y_train_smote)

MultinomialNB(alpha=0.1)

MultinomialNB(alpha=0.1)

# checking best parameters
grid_obj.best_params_

{'alpha': 0.1, 'fit_prior': True}

# Checking train accuracy
model_score_train = Naive_Bayes_tuned.score(X_train_smote, y_train_smote)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = Naive_Bayes_tuned.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.9902834008097166
Test Accuracy: 0.7023809523809523

#predict on test
y_predict = Naive_Bayes_tuned.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.77      0.90      0.83        62
          II       0.29      0.25      0.27         8
         III       0.00      0.00      0.00         6
          IV       0.50      0.17      0.25         6
           V       0.00      0.00      0.00         2

    accuracy                           0.70        84
   macro avg       0.31      0.26      0.27        84
weighted avg       0.63      0.70      0.66        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

from sklearn import metrics

# Function to calculate different metric scores(Accuracy, Recall, Precision, F1-score) of the model trained on balanced training data
def get_metrics_score(model, flag=True):
    '''
    model : classifier to predict values of X
    x_train_smote : Balanced training feature set
    x_test : Test feature set
    y_train_smote : Balanced training target labels
    y_test : Test target labels
    flag : If True, prints the classification report, accuracy, precision, recall, f1-score

    Returns:
    A list of train and test accuracy, precision, recall, F1-score (weighted average) metrics.
    '''
    # defining an empty list to store train and test results
    score_list = []

    # Predicting on train and test sets. We will use the balanced data
    pred_train = model.predict(X_train_smote)
    pred_test = model.predict(X_test)

    # Accuracy of the model
    train_acc = metrics.accuracy_score(y_train_smote, pred_train)
    test_acc = metrics.accuracy_score(y_test, pred_test)

    # Classification report for weighted average precision, recall, f1-score
    classification_report_train = metrics.classification_report(y_train_smote, pred_train, output_dict=True, zero_division=0)
    classification_report_test = metrics.classification_report(y_test, pred_test, output_dict=True, zero_division=0)

    # Weighted averages

    train_precision = classification_report_train['weighted avg']['precision']
    test_precision = classification_report_test['weighted avg']['precision']

    train_recall = classification_report_train['weighted avg']['recall']
    test_recall = classification_report_test['weighted avg']['recall']

    train_f1_score = classification_report_train['weighted avg']['f1-score']
    test_f1_score = classification_report_test['weighted avg']['f1-score']

    # Append all metrics (train/test accuracy, precision, recall, f1-score)
    score_list.extend([train_acc, test_acc, train_precision, test_precision, train_recall, test_recall, train_f1_score, test_f1_score])

    # If the flag is set to True, print out the classification reports and the metrics
    if flag:
        print("Classification Report (Train):\n", metrics.classification_report(y_train, pred_train, zero_division=0))
        print("Classification Report (Test):\n", metrics.classification_report(y_test, pred_test, zero_division=0))
        print("\nMetrics Summary:")
        print(f"Accuracy on Training set: {train_acc}")
        print(f"Accuracy on Test set: {test_acc}")
        print(f"Precision on Training set (Weighted Avg): {train_precision}")
        print(f"Precision on Test set (Weighted Avg): {test_precision}")
        print(f"Recall on Training set (Weighted Avg): {train_recall}")
        print(f"Recall on Test set (Weighted Avg): {test_recall}")
        print(f"F1-Score on Training set (Weighted Avg): {train_f1_score}")
        print(f"F1-Score on Test set (Weighted Avg): {test_f1_score}")

    return score_list  # returning the list with train and test scores

# defining list of models for model comparison
models = [Naive_Bayes_smote, Naive_Bayes_tuned]

# defining empty lists to add train and test results
acc_train = []
acc_test = []
recall_train = []
recall_test = []
precision_train = []
precision_test = []
f1_score_train = []
f1_score_test = []

# looping through all the models to get the accuracy, recall, precision and f-1 scores
for model in models:
    j = get_metrics_score(model,False)
    acc_train.append(np.round(j[0],2))
    acc_test.append(np.round(j[1],2))
    precision_train.append(np.round(j[2],2))
    precision_test.append(np.round(j[3],2))
    recall_train.append(np.round(j[4],2))
    recall_test.append(np.round(j[5],2))
    f1_score_train.append(np.round(j[6],2))
    f1_score_test.append(np.round(j[7],2))

comparison_frame = pd.DataFrame({'Model':['Naive_Bayes_smote', 'Naive_Bayes_tuned'],
                                          'Train_Accuracy': acc_train,'Test_Accuracy': acc_test,
                                          'Train_Recall':recall_train,'Test_Recall':recall_test,
                                          'Train_Precision':precision_train,'Test_Precision':precision_test,
                                          'Train_f1_score':f1_score_train,'Test_f1_score':f1_score_test
                                })
comparison_frame

# Logistic Regression Base Model on original training data
Logistic_Regression = LogisticRegression()
Logistic_Regression.fit(X_train, y_train)
# Checking train accuracy
model_score_train = Logistic_Regression.score(X_train, y_train)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = Logistic_Regression.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.7395209580838323
Test Accuracy: 0.7380952380952381

#predict on test
y_predict = Logistic_Regression.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.74      1.00      0.85        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# Logistic Regression Base Model on balanced training data
Logistic_Regression_smote = LogisticRegression()
Logistic_Regression_smote.fit(X_train_smote, y_train_smote)
# Checking train accuracy
model_score_train = Logistic_Regression_smote.score(X_train_smote, y_train_smote)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = Logistic_Regression_smote.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.994331983805668
Test Accuracy: 0.7023809523809523

#predict on test
y_predict = Logistic_Regression_smote.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

              precision    recall  f1-score   support

           I       0.79      0.89      0.83        62
          II       0.25      0.25      0.25         8
         III       0.25      0.17      0.20         6
          IV       0.50      0.17      0.25         6
           V       0.00      0.00      0.00         2

    accuracy                           0.70        84
   macro avg       0.36      0.29      0.31        84
weighted avg       0.66      0.70      0.67        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# grid search
# Choose the type of classifier.
Logistic_Regression_tuned = LogisticRegression(random_state=1)

# Grid of parameters to choose from
parameters = {'penalty': ['l1', 'l2', 'elasticnet', None],
              'C': [1.0, 10.0, 100.0],
              'solver' : ['lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'],
              'multi_class' : ['auto', 'ovr', 'multinomial']
             }

# Type of scoring used to compare parameter combinations
# acc_scorer = metrics.make_scorer(metrics.recall_score)


# Run the grid search
grid_obj = GridSearchCV(Logistic_Regression_tuned, parameters,cv=5) # , scoring=acc_scorer - # by default the scoring metrics would be the accuracy
# Used 5-fold cross validation
grid_obj = grid_obj.fit(X_train_smote, y_train_smote)

# Set the model to the best combination of parameters
Logistic_Regression_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
Logistic_Regression_tuned.fit(X_train_smote, y_train_smote)

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_glm\_newton_solver.py:498: LinAlgWarning:

The inner solver of NewtonCholeskySolver stumbled upon a singular or very ill-conditioned Hessian matrix at iteration #1. It will now resort to lbfgs instead.
Further options are to use another solver or to avoid such situation in the first place. Possible remedies are removing collinear features of X or increasing the penalization strengths.
The original Linear Algebra message was:
Matrix is singular.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1186: UserWarning:

Setting penalty=None will ignore the C and l1_ratio parameters

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning:


555 fits failed out of a total of 1080.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
45 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1172, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 67, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or None penalties, got l1 penalty.

--------------------------------------------------------------------------------
45 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1172, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 67, in _check_solver
    raise ValueError(
ValueError: Solver newton-cg supports only 'l2' or None penalties, got l1 penalty.

--------------------------------------------------------------------------------
45 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1172, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 67, in _check_solver
    raise ValueError(
ValueError: Solver newton-cholesky supports only 'l2' or None penalties, got l1 penalty.

--------------------------------------------------------------------------------
45 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1172, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 67, in _check_solver
    raise ValueError(
ValueError: Solver sag supports only 'l2' or None penalties, got l1 penalty.

--------------------------------------------------------------------------------
45 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1172, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 67, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or None penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
45 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1172, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 75, in _check_solver
    raise ValueError(
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
45 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1172, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 67, in _check_solver
    raise ValueError(
ValueError: Solver newton-cg supports only 'l2' or None penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
45 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1172, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 67, in _check_solver
    raise ValueError(
ValueError: Solver newton-cholesky supports only 'l2' or None penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
45 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1172, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 67, in _check_solver
    raise ValueError(
ValueError: Solver sag supports only 'l2' or None penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
45 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1182, in fit
    raise ValueError("l1_ratio must be specified when penalty is elasticnet.")
ValueError: l1_ratio must be specified when penalty is elasticnet.

--------------------------------------------------------------------------------
45 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1172, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 80, in _check_solver
    raise ValueError("penalty=None is not supported for the liblinear solver")
ValueError: penalty=None is not supported for the liblinear solver

--------------------------------------------------------------------------------
30 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1212, in fit
    multi_class = _check_multi_class(self.multi_class, solver, len(self.classes_))
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 99, in _check_multi_class
    raise ValueError("Solver %s does not support a multinomial backend." % solver)
ValueError: Solver liblinear does not support a multinomial backend.

--------------------------------------------------------------------------------
30 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1212, in fit
    multi_class = _check_multi_class(self.multi_class, solver, len(self.classes_))
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 99, in _check_multi_class
    raise ValueError("Solver %s does not support a multinomial backend." % solver)
ValueError: Solver newton-cholesky does not support a multinomial backend.


C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1051: UserWarning:

One or more of the test scores are non-finite: [       nan 0.91902834        nan        nan        nan 0.90931174
 0.97732794 0.96761134 0.97732794 0.96923077 0.97732794 0.97732794
        nan        nan        nan        nan        nan        nan
 0.97651822        nan 0.95870445 0.97165992 0.97246964 0.97327935
        nan 0.91902834        nan        nan        nan 0.91902834
 0.96923077 0.96761134 0.96923077 0.96923077 0.96923077 0.96761134
        nan        nan        nan        nan        nan        nan
 0.97165992        nan 0.95303644 0.97165992 0.97004049 0.97165992
        nan        nan        nan        nan        nan 0.90931174
 0.97732794        nan 0.97732794        nan 0.97732794 0.97732794
        nan        nan        nan        nan        nan        nan
 0.97651822        nan 0.95870445        nan 0.97246964 0.97327935
        nan 0.96923077        nan        nan        nan 0.97246964
 0.9805668  0.97732794 0.9805668  0.97732794 0.9805668  0.97813765
        nan        nan        nan        nan        nan        nan
 0.97651822        nan 0.95870445 0.97165992 0.97246964 0.97327935
        nan 0.96923077        nan        nan        nan 0.97246964
 0.97651822 0.97732794 0.97732794 0.97732794 0.97732794 0.9757085
        nan        nan        nan        nan        nan        nan
 0.97165992        nan 0.95303644 0.97165992 0.97004049 0.97165992
        nan        nan        nan        nan        nan 0.97246964
 0.9805668         nan 0.9805668         nan 0.9805668  0.97813765
        nan        nan        nan        nan        nan        nan
 0.97651822        nan 0.95870445        nan 0.97246964 0.97327935
        nan 0.96194332        nan        nan        nan 0.97165992
 0.97894737 0.97732794 0.97813765 0.97732794 0.9757085  0.97408907
        nan        nan        nan        nan        nan        nan
 0.97651822        nan 0.95870445 0.97165992 0.97246964 0.97327935
        nan 0.96194332        nan        nan        nan 0.97246964
 0.9757085  0.97732794 0.97732794 0.97732794 0.97327935 0.97327935
        nan        nan        nan        nan        nan        nan
 0.97165992        nan 0.95303644 0.97165992 0.97004049 0.97165992
        nan        nan        nan        nan        nan 0.97165992
 0.97894737        nan 0.97813765        nan 0.9757085  0.97408907
        nan        nan        nan        nan        nan        nan
 0.97651822        nan 0.95870445        nan 0.97246964 0.97327935]

LogisticRegression(C=10.0, random_state=1)

LogisticRegression(C=10.0, random_state=1)

# checking best parameters
grid_obj.best_params_

{'C': 10.0, 'multi_class': 'auto', 'penalty': 'l2', 'solver': 'lbfgs'}

# Checking train accuracy
model_score_train = Logistic_Regression_tuned.score(X_train_smote, y_train_smote)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = Logistic_Regression_tuned.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.9975708502024292
Test Accuracy: 0.7023809523809523

#predict on test
y_predict = Logistic_Regression_tuned.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.78      0.90      0.84        62
          II       0.17      0.12      0.14         8
         III       0.25      0.17      0.20         6
          IV       0.50      0.17      0.25         6
           V       0.00      0.00      0.00         2

    accuracy                           0.70        84
   macro avg       0.34      0.27      0.29        84
weighted avg       0.64      0.70      0.66        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# defining list of models for model comparison
models = [Logistic_Regression_smote, Logistic_Regression_tuned]

# defining empty lists to add train and test results
acc_train = []
acc_test = []
recall_train = []
recall_test = []
precision_train = []
precision_test = []
f1_score_train = []
f1_score_test = []

# looping through all the models to get the accuracy, recall, precision and f-1 scores
for model in models:
    j = get_metrics_score(model,False)
    acc_train.append(np.round(j[0],2))
    acc_test.append(np.round(j[1],2))
    precision_train.append(np.round(j[2],2))
    precision_test.append(np.round(j[3],2))
    recall_train.append(np.round(j[4],2))
    recall_test.append(np.round(j[5],2))
    f1_score_train.append(np.round(j[6],2))
    f1_score_test.append(np.round(j[7],2))

comparison_frame = pd.DataFrame({'Model':['Logistic_Regression_smote', 'Logistic_Regression_tuned'],
                                          'Train_Accuracy': acc_train,'Test_Accuracy': acc_test,
                                          'Train_Recall':recall_train,'Test_Recall':recall_test,
                                          'Train_Precision':precision_train,'Test_Precision':precision_test,
                                          'Train_f1_score':f1_score_train,'Test_f1_score':f1_score_test
                                })
comparison_frame

from sklearn import svm

# SVM Base Model on original training data
SVM = svm.SVC()
SVM.fit(X_train, y_train)
# Checking train accuracy
model_score_train = SVM.score(X_train, y_train)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = SVM.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.7724550898203593
Test Accuracy: 0.7380952380952381

#predict on test
y_predict = SVM.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.74      1.00      0.85        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# SVM Base Model on balanced training data
SVM_smote = svm.SVC()
SVM_smote.fit(X_train_smote, y_train_smote)
# Checking train accuracy
model_score_train = SVM_smote.score(X_train_smote, y_train_smote)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = SVM_smote.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.9975708502024292
Test Accuracy: 0.7380952380952381

#predict on test
y_predict = SVM_smote.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.75      1.00      0.86        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.55      0.74      0.63        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# grid search
# Choose the type of classifier.
SVM_tuned = svm.SVC(random_state=1)

# Grid of parameters to choose from
parameters = {'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
              'C': [1.0, 10.0, 100.0],
              'degree': [2,3,4],
              'gamma' : ['scale', 'auto'],
              'decision_function_shape' : ['ovo', 'ovr']
             }

# Type of scoring used to compare parameter combinations
# acc_scorer = metrics.make_scorer(metrics.recall_score)


# Run the grid search
grid_obj = GridSearchCV(SVM_tuned, parameters,cv=5) # , scoring=acc_scorer - # by default the scoring metrics would be the accuracy
# Used 5-fold cross validation
grid_obj = grid_obj.fit(X_train_smote, y_train_smote)

# Set the model to the best combination of parameters
SVM_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
SVM_tuned.fit(X_train_smote, y_train_smote)

SVC(decision_function_shape='ovo', degree=2, random_state=1)

SVC(decision_function_shape='ovo', degree=2, random_state=1)

# checking best parameters
grid_obj.best_params_

{'C': 1.0,
 'decision_function_shape': 'ovo',
 'degree': 2,
 'gamma': 'scale',
 'kernel': 'rbf'}

# Checking train accuracy
model_score_train = SVM_tuned.score(X_train_smote, y_train_smote)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = SVM_tuned.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.9975708502024292
Test Accuracy: 0.7380952380952381

#predict on test
y_predict = SVM_tuned.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.75      1.00      0.86        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.55      0.74      0.63        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# defining list of models for model comparison
models = [SVM_smote, SVM_tuned]

# defining empty lists to add train and test results
acc_train = []
acc_test = []
recall_train = []
recall_test = []
precision_train = []
precision_test = []
f1_score_train = []
f1_score_test = []

# looping through all the models to get the accuracy, recall, precision and f-1 scores
for model in models:
    j = get_metrics_score(model,False)
    acc_train.append(np.round(j[0],2))
    acc_test.append(np.round(j[1],2))
    precision_train.append(np.round(j[2],2))
    precision_test.append(np.round(j[3],2))
    recall_train.append(np.round(j[4],2))
    recall_test.append(np.round(j[5],2))
    f1_score_train.append(np.round(j[6],2))
    f1_score_test.append(np.round(j[7],2))

# Creating dataframe for all the metrics for the models listed

comparison_frame = pd.DataFrame({'Model':['SVM_smote', 'SVM_tuned'],
                                          'Train_Accuracy': acc_train,'Test_Accuracy': acc_test,
                                          'Train_Recall':recall_train,'Test_Recall':recall_test,
                                          'Train_Precision':precision_train,'Test_Precision':precision_test,
                                          'Train_f1_score':f1_score_train,'Test_f1_score':f1_score_test
                                })
comparison_frame

# Random Forest Base Model on original training data
Random_Forest = RandomForestClassifier(random_state=1)
Random_Forest.fit(X_train, y_train)
# Checking train accuracy
model_score_train = Random_Forest.score(X_train, y_train)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = Random_Forest.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.9970059880239521
Test Accuracy: 0.75

#predict on test
y_predict = Random_Forest.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.76      1.00      0.86        62
          II       0.50      0.12      0.20         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.75        84
   macro avg       0.25      0.23      0.21        84
weighted avg       0.61      0.75      0.65        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# Random forest Base Model on balanced training data
Random_Forest_smote = RandomForestClassifier(random_state=1)
Random_Forest_smote.fit(X_train_smote, y_train_smote)
# Checking train accuracy
model_score_train = Random_Forest_smote.score(X_train_smote, y_train_smote)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = Random_Forest_smote.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.9991902834008097
Test Accuracy: 0.7380952380952381

#predict on test
y_predict = Random_Forest_smote.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.76      1.00      0.86        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.56      0.74      0.64        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# grid search
# Choose the type of classifier.
Random_Forest_tuned = RandomForestClassifier(random_state=1)

# Grid of parameters to choose from
parameters = {"n_estimators": [100,150,200,250],
    "min_samples_leaf": [5, 10],
    "max_features": [0.2, 0.7],
    "max_samples": [0.3, 0.7]
             }

# Type of scoring used to compare parameter combinations
# acc_scorer = metrics.make_scorer(metrics.recall_score)


# Run the grid search
grid_obj = GridSearchCV(Random_Forest_tuned, parameters,cv=5) # , scoring=acc_scorer - # by default the scoring metrics would be the accuracy
# Used 5-fold cross validation
grid_obj = grid_obj.fit(X_train_smote, y_train_smote)

# Set the model to the best combination of parameters
Random_Forest_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
Random_Forest_tuned.fit(X_train_smote, y_train_smote)

RandomForestClassifier(max_features=0.2, max_samples=0.7, min_samples_leaf=5,
                       n_estimators=150, random_state=1)

RandomForestClassifier(max_features=0.2, max_samples=0.7, min_samples_leaf=5,
                       n_estimators=150, random_state=1)

# checking best parameters
grid_obj.best_params_

{'max_features': 0.2,
 'max_samples': 0.7,
 'min_samples_leaf': 5,
 'n_estimators': 150}

# Checking train accuracy
model_score_train = Random_Forest_tuned.score(X_train_smote, y_train_smote)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = Random_Forest_tuned.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.982995951417004
Test Accuracy: 0.7142857142857143

#predict on test
y_predict = Random_Forest_tuned.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.79      0.94      0.86        62
          II       0.00      0.00      0.00         8
         III       0.67      0.33      0.44         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.71        84
   macro avg       0.29      0.25      0.26        84
weighted avg       0.63      0.71      0.67        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# grid search
# Choose the type of classifier.
Random_Forest_tuned = RandomForestClassifier(random_state=1)

# Grid of parameters to choose from
parameters = {"n_estimators": [100,150,200,250],
    "min_samples_leaf": [5, 10],
    "max_features": [0.2, 0.7],
    "max_samples": [0.3, 0.7]
             }

# Type of scoring used to compare parameter combinations
# acc_scorer = metrics.make_scorer(metrics.recall_score)


# Run the grid search
grid_obj = GridSearchCV(Random_Forest_tuned, parameters,cv=5) # , scoring=acc_scorer - # by default the scoring metrics would be the accuracy
# Used 5-fold cross validation
grid_obj = grid_obj.fit(X_train_smote, y_train_smote)

# Set the model to the best combination of parameters
Random_Forest_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
Random_Forest_tuned.fit(X_train_smote, y_train_smote)

RandomForestClassifier(max_features=0.2, max_samples=0.7, min_samples_leaf=5,
                       n_estimators=150, random_state=1)

RandomForestClassifier(max_features=0.2, max_samples=0.7, min_samples_leaf=5,
                       n_estimators=150, random_state=1)

# defining list of models for model comparison
models = [Random_Forest_smote, Random_Forest_tuned]

# defining empty lists to add train and test results
acc_train = []
acc_test = []
recall_train = []
recall_test = []
precision_train = []
precision_test = []
f1_score_train = []
f1_score_test = []

# looping through all the models to get the accuracy, recall, precision and f-1 scores
for model in models:
    j = get_metrics_score(model,False)
    acc_train.append(np.round(j[0],2))
    acc_test.append(np.round(j[1],2))
    precision_train.append(np.round(j[2],2))
    precision_test.append(np.round(j[3],2))
    recall_train.append(np.round(j[4],2))
    recall_test.append(np.round(j[5],2))
    f1_score_train.append(np.round(j[6],2))
    f1_score_test.append(np.round(j[7],2))

comparison_frame = pd.DataFrame({'Model':['Random_Forest_smote', 'Random_Forest_tuned'],
                                          'Train_Accuracy': acc_train,'Test_Accuracy': acc_test,
                                          'Train_Recall':recall_train,'Test_Recall':recall_test,
                                          'Train_Precision':precision_train,'Test_Precision':precision_test,
                                          'Train_f1_score':f1_score_train,'Test_f1_score':f1_score_test
                                })
comparison_frame

# Gradient Boost Base Model on original training data
GradientBoost = GradientBoostingClassifier(random_state=1)
GradientBoost.fit(X_train, y_train)
# Checking train accuracy
model_score_train = GradientBoost.score(X_train, y_train)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = GradientBoost.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.9970059880239521
Test Accuracy: 0.6904761904761905

#predict on test
y_predict = GradientBoost.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.75      0.92      0.83        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.25      0.17      0.20         6
           V       0.00      0.00      0.00         2

    accuracy                           0.69        84
   macro avg       0.20      0.22      0.21        84
weighted avg       0.57      0.69      0.62        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# Gradient Boost Base Model on balanced training data
GradientBoost_smote = GradientBoostingClassifier(random_state=1)
GradientBoost_smote.fit(X_train_smote, y_train_smote)
# Checking train accuracy
model_score_train = GradientBoost_smote.score(X_train_smote, y_train_smote)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = GradientBoost_smote.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.9991902834008097
Test Accuracy: 0.7023809523809523

#predict on test
y_predict = GradientBoost_smote.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.81      0.94      0.87        62
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.50      0.17      0.25         6
           V       0.00      0.00      0.00         2

    accuracy                           0.70        84
   macro avg       0.26      0.22      0.22        84
weighted avg       0.63      0.70      0.66        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# grid search
# Choose the type of classifier.
GradientBoost_tuned = GradientBoostingClassifier(random_state=1)

# Grid of parameters to choose from
parameters = {
    "loss": ['exponential', 'log_loss'],
    "learning_rate": [0.1, 1.0,10.0],
    "n_estimators": [50,100,200],
    "subsample": [0.1, 0.5, 1.0],
    "max_depth": [1,3,5],
             }

# Type of scoring used to compare parameter combinations
# acc_scorer = metrics.make_scorer(metrics.recall_score)


# Run the grid search
grid_obj = GridSearchCV(GradientBoost_tuned, parameters,cv=5) # , scoring=acc_scorer - # by default the scoring metrics would be the accuracy
# Used 5-fold cross validation
grid_obj = grid_obj.fit(X_train_smote, y_train_smote)

# Set the model to the best combination of parameters
GradientBoost_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
GradientBoost_tuned.fit(X_train_smote, y_train_smote)

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:547: FitFailedWarning:


405 fits failed out of a total of 810.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
405 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 895, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\base.py", line 1474, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\ensemble\_gb.py", line 673, in fit
    self._loss = self._get_loss(sample_weight=sample_weight)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\anime\anaconda3\Lib\site-packages\sklearn\ensemble\_gb.py", line 1537, in _get_loss
    raise ValueError(
ValueError: loss='exponential' is only suitable for a binary classification problem, you have n_classes=5. Please use loss='log_loss' instead.


C:\Users\anime\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1051: UserWarning:

One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.90040486 0.91740891 0.91983806
 0.93522267 0.93360324 0.93927126 0.9465587  0.94574899 0.94251012
 0.94493927 0.9465587  0.94251012 0.95546559 0.94817814 0.94898785
 0.96437247 0.95465587 0.95060729 0.9465587  0.9562753  0.93927126
 0.9659919  0.95708502 0.94979757 0.97408907 0.9611336  0.95384615
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.16032389 0.90040486 0.92712551
 0.16032389 0.90202429 0.94493927 0.16032389 0.76923077 0.94979757
 0.2097166  0.91740891 0.94493927 0.2097166  0.92712551 0.95789474
 0.2097166  0.93522267 0.95870445 0.14736842 0.94574899 0.951417
 0.1465587  0.951417   0.95708502 0.1465587  0.95708502 0.95789474
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.23643725 0.21214575 0.29230769
 0.23643725 0.21214575 0.29230769 0.23643725 0.21214575 0.29230769
 0.25587045 0.12226721 0.11497976 0.25587045 0.12226721 0.11497976
 0.25587045 0.12226721 0.11497976 0.18866397 0.09392713 0.09959514
 0.18866397 0.09392713 0.09959514 0.20890688 0.09392713 0.09959514]

GradientBoostingClassifier(max_depth=5, n_estimators=200, random_state=1,
                           subsample=0.1)

GradientBoostingClassifier(max_depth=5, n_estimators=200, random_state=1,
                           subsample=0.1)

# checking best parameters
grid_obj.best_params_

{'learning_rate': 0.1,
 'loss': 'log_loss',
 'max_depth': 5,
 'n_estimators': 200,
 'subsample': 0.1}

# Checking train accuracy
model_score_train = GradientBoost_tuned.score(X_train_smote, y_train_smote)
print('Train Accuracy:', model_score_train)
# Checking test accuracy
model_score_test = GradientBoost_tuned.score(X_test, y_test)
print('Test Accuracy:', model_score_test)

Train Accuracy: 0.9983805668016195
Test Accuracy: 0.7261904761904762

#predict on test
y_predict = GradientBoost_tuned.predict(X_test)
# Performance metrics for base model on test data
print(metrics.classification_report(y_test, y_predict))

              precision    recall  f1-score   support

           I       0.82      0.95      0.88        62
          II       0.11      0.12      0.12         8
         III       0.33      0.17      0.22         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.73        84
   macro avg       0.25      0.25      0.24        84
weighted avg       0.64      0.73      0.68        84

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

C:\Users\anime\anaconda3\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning:

Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

# defining list of models for model comparison
models = [GradientBoost_smote, GradientBoost_tuned]

# defining empty lists to add train and test results
acc_train = []
acc_test = []
recall_train = []
recall_test = []
precision_train = []
precision_test = []
f1_score_train = []
f1_score_test = []

# looping through all the models to get the accuracy, recall, precision and f-1 scores
for model in models:
    j = get_metrics_score(model,False)
    acc_train.append(np.round(j[0],2))
    acc_test.append(np.round(j[1],2))
    precision_train.append(np.round(j[2],2))
    precision_test.append(np.round(j[3],2))
    recall_train.append(np.round(j[4],2))
    recall_test.append(np.round(j[5],2))
    f1_score_train.append(np.round(j[6],2))
    f1_score_test.append(np.round(j[7],2))

comparison_frame = pd.DataFrame({'Model':['GradientBoost_smote', 'GradientBoost_tuned'],
                                          'Train_Accuracy': acc_train,'Test_Accuracy': acc_test,
                                          'Train_Recall':recall_train,'Test_Recall':recall_test,
                                          'Train_Precision':precision_train,'Test_Precision':precision_test,
                                          'Train_f1_score':f1_score_train,'Test_f1_score':f1_score_test
                                })
comparison_frame

# defining list of models
models = [Naive_Bayes_smote, Naive_Bayes_tuned, Logistic_Regression_smote, Logistic_Regression_tuned, SVM_smote, SVM_tuned, Random_Forest_smote, Random_Forest_tuned,  GradientBoost_smote, GradientBoost_tuned]

# defining empty lists to add train and test results
acc_train = []
acc_test = []
recall_train = []
recall_test = []
precision_train = []
precision_test = []
f1_score_train = []
f1_score_test = []

# looping through all the models to get the accuracy, recall, precision and f-1 scores
for model in models:
    j = get_metrics_score(model,False)
    acc_train.append(np.round(j[0],2))
    acc_test.append(np.round(j[1],2))
    precision_train.append(np.round(j[2],2))
    precision_test.append(np.round(j[3],2))
    recall_train.append(np.round(j[4],2))
    recall_test.append(np.round(j[5],2))
    f1_score_train.append(np.round(j[6],2))
    f1_score_test.append(np.round(j[7],2))

comparison_frame = pd.DataFrame({'Model':['Naive_Bayes_smote', 'Naive_Bayes_tuned', 'Logistic_Regression_smote', 'Logistic_Regression_tuned', 'SVM_smote', 'SVM_tuned', 'Random_Forest_smote', 'Random_Forest_tuned', 'GradientBoost_smote', 'GradientBoost_tuned'],
                                          'Train_Accuracy': acc_train,'Test_Accuracy': acc_test,
                                          'Train_Recall':recall_train,'Test_Recall':recall_test,
                                          'Train_Precision':precision_train,'Test_Precision':precision_test,
                                          'Train_f1_score':f1_score_train,'Test_f1_score':f1_score_test})
comparison_frame

from sklearn import metrics

# Function to calculate different metric scores(Accuracy, Recall, Precision, F1-score) of the base model trained on imbalanced data
def get_metrics_score_base_model(model, flag=True):
    '''
    model : classifier to predict values of X
    x_train_smote : Balanced training feature set
    x_test : Test feature set
    y_train_smote : Balanced training target labels
    y_test : Test target labels
    flag : If True, prints the classification report, accuracy, precision, recall, f1-score

    Returns:
    A list of train and test accuracy, precision, recall, F1-score (weighted average) metrics.
    '''
    # defining an empty list to store train and test results
    score_list = []

    # Predicting on train and test sets. We will use the balanced data
    pred_train = model.predict(X_train)
    pred_test = model.predict(X_test)

    # Accuracy of the model
    train_acc = metrics.accuracy_score(y_train, pred_train)
    test_acc = metrics.accuracy_score(y_test, pred_test)

    # Classification report for weighted average precision, recall, f1-score
    classification_report_train = metrics.classification_report(y_train, pred_train, output_dict=True, zero_division=0)
    classification_report_test = metrics.classification_report(y_test, pred_test, output_dict=True, zero_division=0)

    # Weighted averages

    train_precision = classification_report_train['weighted avg']['precision']
    test_precision = classification_report_test['weighted avg']['precision']

    train_recall = classification_report_train['weighted avg']['recall']
    test_recall = classification_report_test['weighted avg']['recall']

    train_f1_score = classification_report_train['weighted avg']['f1-score']
    test_f1_score = classification_report_test['weighted avg']['f1-score']

    # Append all metrics (train/test accuracy, precision, recall, f1-score)
    score_list.extend([train_acc, test_acc, train_precision, test_precision, train_recall, test_recall, train_f1_score, test_f1_score])

    # If the flag is set to True, print out the classification reports and the metrics
    if flag:
        print("Classification Report (Train):\n", metrics.classification_report(y_train, pred_train, zero_division=0))
        print("Classification Report (Test):\n", metrics.classification_report(y_test, pred_test, zero_division=0))
        print("\nMetrics Summary:")
        print(f"Accuracy on Training set: {train_acc}")
        print(f"Accuracy on Test set: {test_acc}")
        print(f"Precision on Training set (Weighted Avg): {train_precision}")
        print(f"Precision on Test set (Weighted Avg): {test_precision}")
        print(f"Recall on Training set (Weighted Avg): {train_recall}")
        print(f"Recall on Test set (Weighted Avg): {test_recall}")
        print(f"F1-Score on Training set (Weighted Avg): {train_f1_score}")
        print(f"F1-Score on Test set (Weighted Avg): {test_f1_score}")

    return score_list  # returning the list with train and test scores
# ----------------------------------------------------------------------------

# defining list of base models for model comparison
models = [Naive_Bayes, Logistic_Regression, SVM, Random_Forest, GradientBoost]

# defining empty lists to add train and test results
acc_train = []
acc_test = []
recall_train = []
recall_test = []
precision_train = []
precision_test = []
f1_score_train = []
f1_score_test = []

# looping through all the models to get the accuracy, recall, precision and f-1 scores
for model in models:
    j = get_metrics_score_base_model(model,False)
    acc_train.append(np.round(j[0],2))
    acc_test.append(np.round(j[1],2))
    precision_train.append(np.round(j[2],2))
    precision_test.append(np.round(j[3],2))
    recall_train.append(np.round(j[4],2))
    recall_test.append(np.round(j[5],2))
    f1_score_train.append(np.round(j[6],2))
    f1_score_test.append(np.round(j[7],2))


comparison_frame_base_model = pd.DataFrame({'Model':[ 'Naive_Bayes', 'Logistic_Regression', 'SVM', 'Random_Forest', 'GradientBoost'],
                                          'Train_Accuracy': acc_train,'Test_Accuracy': acc_test,
                                          'Train_Recall':recall_train,'Test_Recall':recall_test,
                                          'Train_Precision':precision_train,'Test_Precision':precision_test,
                                          'Train_f1_score':f1_score_train,'Test_f1_score':f1_score_test
                                })
comparison_frame_base_model

# Concatenating the comparison dataframes to make final comaprisons of metrics for all the models created
Final_Comparison = (pd.concat([comparison_frame_base_model, comparison_frame], ignore_index=True,axis=0))
Final_Comparison

# Ordering the models based on the descending order of Test_Accuracy
Final_Comparison.sort_values("Test_Accuracy", ascending=False) # inplace=True

# Ordering the models based on the descending order of Test_f1_score
Final_Comparison.sort_values("Test_f1_score", ascending=False) # inplace=True

# Mounting Google Drive
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Import the original excel data as dataframe using pandas
# data_original = pd.read_excel("Data+Set+-+industrial_safety_and_health_database_with_accidents_description.xlsx")
data_original = pd.read_excel('/content/drive/MyDrive/Great Learning/Capstone/Data+Set+-+industrial_safety_and_health_database_with_accidents_description.xlsx')
data_original.head()

# Reading the cleaned and NLP pre-processed csv data created in 1st milestone steps as Dataframe
df4 = pd.read_csv('/content/drive/MyDrive/Great Learning/Capstone/cleaned_industrial_safety_data.csv')
df4.head()

# Importing libraries to use transformers models
from transformers import BertTokenizer, BertModel, RobertaTokenizer, RobertaModel
import torch
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder

# Choose a model: 'bert-base-uncased' or 'roberta-base'
model_name = 'bert-base-uncased'  # or 'roberta-base' for RoBERTa

# Initialize tokenizer and model
if 'bert' in model_name:
    tokenizer = BertTokenizer.from_pretrained(model_name)
    model = BertModel.from_pretrained(model_name)
elif 'roberta' in model_name:
    tokenizer = RobertaTokenizer.from_pretrained(model_name)
    model = RobertaModel.from_pretrained(model_name)

def get_embeddings(text_list, tokenizer, model):
    """ Get embeddings for each text in text_list using tokenizer and model """
    with torch.no_grad():
        inputs = tokenizer(text_list, return_tensors="pt", padding=True, truncation=True, max_length=512)
        outputs = model(**inputs)
        embeddings = outputs.last_hidden_state[:, 0, :]  # Get the CLS token embedding
        return embeddings.numpy()

/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: 
The secret `HF_TOKEN` does not exist in your Colab secrets.
To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.
You will be able to reuse this secret in all of your notebooks.
Please note that authentication is recommended but still optional to access public models or datasets.
  warnings.warn(

tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]

vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]

tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]

config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]

/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
  warnings.warn(

model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]

# Get embeddings for descriptions
embeddings = get_embeddings(df4['Preprocessed_Description'].tolist(), tokenizer, model)

# Encode labels to numeric format
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(df4['Accident Level'])

# viewing the embeddings
embeddings

array([[-0.8366512 ,  0.05823708,  0.21985547, ..., -0.05338174,
        -0.1721213 , -0.01900132],
       [-0.77411646,  0.0161009 ,  0.28162766, ..., -0.28561983,
        -0.13525663,  0.719628  ],
       [-0.42129612,  0.14128   ,  0.16086406, ..., -0.21355191,
        -0.2534065 ,  0.27237302],
       ...,
       [-0.32311797,  0.1012077 , -0.01155421, ..., -0.43120098,
         0.01447114,  0.406252  ],
       [-0.40220645,  0.16602494, -0.06106976, ..., -0.54142606,
        -0.02233603,  0.11503189],
       [-0.42237413,  0.10597786,  0.29480186, ..., -0.32330784,
        -0.22980821,  0.339079  ]], dtype=float32)

# viewing the encoded labels
y

array([0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0,
       1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 2, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
       0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 2, 1, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0,
       0, 3, 0, 1, 0, 0, 0, 0, 2, 0, 0, 1, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 3, 0, 0, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0,
       0, 0, 4, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
       3, 0, 0, 0, 0, 0, 0, 4, 2, 0, 0, 3, 0, 2, 2, 3, 2, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 3, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 1, 0, 0, 2, 3, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 1, 0, 2, 0, 1, 2, 3, 0, 0, 1,
       0, 2, 0, 0, 3, 3, 0, 0, 3, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0,
       0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 4, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 3, 0, 1, 0, 2, 3, 0, 0, 0, 0,
       0, 3, 0, 3, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 4, 0, 1, 0, 1, 4,
       4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
       3, 0, 0, 1, 0, 0, 2, 3, 1, 4, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 2, 0, 1, 3, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0])

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(embeddings, y, test_size=0.2, random_state=42)

# Importing Required Libraries
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Train various classification models
classifier = {
    # "Naive_Bayes": MultinomialNB(),
    "Logistic_Regression": LogisticRegression(random_state=1),
    "SVM": SVC(random_state=1),
    "Random_Forest": RandomForestClassifier(random_state=1),
    "Gradient_Boosting": GradientBoostingClassifier(random_state=1)
}

# Initialize an empty list to store classification metrics
metrics_list = []

for model_name, clf in classifier.items():
    # Train the model
    clf.fit(X_train, y_train)

    # Make predictions on the test data
    y_pred = clf.predict(X_test)

    # Get the classification report as a string
    report_str = classification_report(y_test, y_pred, zero_division=0)

    # Get the classification report as a dictionary
    report_dict = classification_report(y_test, y_pred, output_dict=True, zero_division=0)

    # Extract accuracy, precision, recall, and F1-score (average metrics for each model)
    accuracy = accuracy_score(y_test, y_pred)
    precision = report_dict['weighted avg']['precision']
    recall = report_dict['weighted avg']['recall']
    f1_score = report_dict['weighted avg']['f1-score']

    # Append the metrics to the list
    metrics_list.append({
        'Model': model_name,
        'Accuracy': accuracy,
        'Precision': precision,
        'Recall': recall,
        'F1-Score': f1_score
    })

    # Print the classification report for the current model
    print(f"Classification Report for {model_name}:")
    print(report_str)
    print("=" * 60)  # Divider for clarity

# Convert the list of metrics into a DataFrame for comparison
metrics_comparison = pd.DataFrame(metrics_list)

# Display the comparison of metrics for each model
print("Comparison of Metrics:")
print(metrics_comparison)

/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Classification Report for Logistic_Regression:
              precision    recall  f1-score   support

           0       0.74      0.92      0.82        63
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         4
           3       0.00      0.00      0.00         7
           4       0.00      0.00      0.00         2

    accuracy                           0.69        84
   macro avg       0.15      0.18      0.16        84
weighted avg       0.56      0.69      0.62        84

============================================================
Classification Report for SVM:
              precision    recall  f1-score   support

           0       0.75      1.00      0.86        63
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         4
           3       0.00      0.00      0.00         7
           4       0.00      0.00      0.00         2

    accuracy                           0.75        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.56      0.75      0.64        84

============================================================
Classification Report for Random_Forest:
              precision    recall  f1-score   support

           0       0.75      1.00      0.86        63
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         4
           3       0.00      0.00      0.00         7
           4       0.00      0.00      0.00         2

    accuracy                           0.75        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.56      0.75      0.64        84

============================================================
Classification Report for Gradient_Boosting:
              precision    recall  f1-score   support

           0       0.76      0.90      0.83        63
           1       0.29      0.25      0.27         8
           2       0.00      0.00      0.00         4
           3       0.00      0.00      0.00         7
           4       0.00      0.00      0.00         2

    accuracy                           0.70        84
   macro avg       0.21      0.23      0.22        84
weighted avg       0.60      0.70      0.64        84

============================================================
Comparison of Metrics:
                 Model  Accuracy  Precision    Recall  F1-Score
0  Logistic_Regression  0.690476   0.557692  0.690476  0.617021
1                  SVM  0.750000   0.562500  0.750000  0.642857
2        Random_Forest  0.750000   0.562500  0.750000  0.642857
3    Gradient_Boosting  0.702381   0.597211  0.702381  0.644962

# Get embeddings for raw descriptions
embeddings = get_embeddings(data_original['Description'].tolist(), tokenizer, model)

# Encode labels to numeric format
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(data_original['Accident Level'])

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(embeddings, y, test_size=0.2, random_state=42)

# Train various classification models on raw data
classifier = {
    # "Naive_Bayes": MultinomialNB(),
    "Logistic_Regression": LogisticRegression(random_state=1),
    "SVM": SVC(random_state=1),
    "Random_Forest": RandomForestClassifier(random_state=1),
    "Gradient_Boosting": GradientBoostingClassifier(random_state=1)
}

# Initialize an empty list to store classification metrics
metrics_list = []

for model_name, clf in classifier.items():
    # Train the model
    clf.fit(X_train, y_train)

    # Make predictions on the test data
    y_pred = clf.predict(X_test)

    # Get the classification report as a string
    report_str = classification_report(y_test, y_pred, zero_division=0)

    # Get the classification report as a dictionary
    report_dict = classification_report(y_test, y_pred, output_dict=True, zero_division=0)

    # Extract accuracy, precision, recall, and F1-score (average metrics for each model)
    accuracy = accuracy_score(y_test, y_pred)
    precision = report_dict['weighted avg']['precision']
    recall = report_dict['weighted avg']['recall']
    f1_score = report_dict['weighted avg']['f1-score']

    # Append the metrics to the list
    metrics_list.append({
        'Model': model_name,
        'Accuracy': accuracy,
        'Precision': precision,
        'Recall': recall,
        'F1-Score': f1_score
    })

    # Print the classification report for the current model
    print(f"Classification Report for {model_name}:")
    print(report_str)
    print("=" * 60)  # Divider for clarity

# Convert the list of metrics into a DataFrame for comparison
metrics_comparison = pd.DataFrame(metrics_list)

# Display the comparison of metrics for each model
print("Comparison of Metrics:")
print(metrics_comparison)

/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Classification Report for Logistic_Regression:
              precision    recall  f1-score   support

           0       0.83      0.94      0.88        68
           1       0.20      0.17      0.18         6
           2       0.00      0.00      0.00         5
           3       0.00      0.00      0.00         4
           4       0.00      0.00      0.00         2

    accuracy                           0.76        85
   macro avg       0.21      0.22      0.21        85
weighted avg       0.68      0.76      0.72        85

============================================================
Classification Report for SVM:
              precision    recall  f1-score   support

           0       0.80      1.00      0.89        68
           1       0.00      0.00      0.00         6
           2       0.00      0.00      0.00         5
           3       0.00      0.00      0.00         4
           4       0.00      0.00      0.00         2

    accuracy                           0.80        85
   macro avg       0.16      0.20      0.18        85
weighted avg       0.64      0.80      0.71        85

============================================================
Classification Report for Random_Forest:
              precision    recall  f1-score   support

           0       0.80      1.00      0.89        68
           1       0.00      0.00      0.00         6
           2       0.00      0.00      0.00         5
           3       0.00      0.00      0.00         4
           4       0.00      0.00      0.00         2

    accuracy                           0.80        85
   macro avg       0.16      0.20      0.18        85
weighted avg       0.64      0.80      0.71        85

============================================================
Classification Report for Gradient_Boosting:
              precision    recall  f1-score   support

           0       0.79      0.93      0.85        68
           1       0.00      0.00      0.00         6
           2       0.00      0.00      0.00         5
           3       0.00      0.00      0.00         4
           4       0.00      0.00      0.00         2

    accuracy                           0.74        85
   macro avg       0.16      0.19      0.17        85
weighted avg       0.63      0.74      0.68        85

============================================================
Comparison of Metrics:
                 Model  Accuracy  Precision    Recall  F1-Score
0  Logistic_Regression  0.764706   0.679053  0.764706  0.719041
1                  SVM  0.800000   0.640000  0.800000  0.711111
2        Random_Forest  0.800000   0.640000  0.800000  0.711111
3    Gradient_Boosting  0.741176   0.630000  0.741176  0.681081

# Mounting Google Drive
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

# importing basic libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Import the original excel data as dataframe using pandas
# data_original = pd.read_excel("Data+Set+-+industrial_safety_and_health_database_with_accidents_description.xlsx")
data_original = pd.read_excel('/content/drive/MyDrive/Great Learning/Capstone/Data+Set+-+industrial_safety_and_health_database_with_accidents_description.xlsx')
data_original.head()

!pip install datasets

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      Successfully uninstalled fsspec-2024.10.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
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Successfully installed datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0

import torch
from transformers import (
    BertTokenizer, BertForSequenceClassification,
    RobertaTokenizer, RobertaForSequenceClassification,
    DistilBertTokenizer, DistilBertForSequenceClassification,
    XLNetTokenizer, XLNetForSequenceClassification,
    AlbertTokenizer, AlbertForSequenceClassification,
    Trainer, TrainingArguments
)
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from datasets import Dataset
import pandas as pd

# Load data
df = data_original.copy()
df = df[['Description', 'Accident Level']]  # Columns with text and labels

# Encode labels
label_encoder = LabelEncoder()
df['labels'] = label_encoder.fit_transform(df['Accident Level'])

# Split data into train and test
train_texts, test_texts, train_labels, test_labels = train_test_split(
    df['Description'].tolist(), df['labels'].tolist(), test_size=0.2, random_state=42
)

import warnings
import pandas as pd
from transformers import Trainer, TrainingArguments
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from datasets import Dataset

# Suppress all warnings
warnings.filterwarnings("ignore")

# Define a custom metric function to calculate accuracy, precision, recall, and F1
def compute_metrics(pred):
    labels = pred.label_ids
    preds = pred.predictions.argmax(-1)  # Get the index of the highest prediction score
    accuracy = accuracy_score(labels, preds)
    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average="weighted")
    return {
        'accuracy': accuracy,
        'precision': precision,
        'recall': recall,
        'f1': f1,
    }

# Update training function to log training loss and evaluation accuracy, and store metrics for each epoch
def train_model(model_name, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels, label_encoder):
    tokenizer = tokenizer_class.from_pretrained(model_name)
    model = model_class.from_pretrained(model_name, num_labels=len(label_encoder.classes_))

    # Tokenize the data
    train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=128)
    test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=128)

    # Convert to Hugging Face Dataset format
    train_dataset = Dataset.from_dict({'input_ids': train_encodings['input_ids'], 'attention_mask': train_encodings['attention_mask'], 'labels': train_labels})
    test_dataset = Dataset.from_dict({'input_ids': test_encodings['input_ids'], 'attention_mask': test_encodings['attention_mask'], 'labels': test_labels})

    # Define training arguments with logging enabled
    training_args = TrainingArguments(
        output_dir='./results',
        evaluation_strategy="epoch",
        logging_strategy="steps",
        logging_steps=10,  # Log every 10 steps
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        num_train_epochs=3,
        weight_decay=0.01,
        logging_dir='./logs',
        save_strategy="no",


        # Added these lines
        # load_best_model_at_end=True, # loads the best model according to metric_for_best_model
        # metric_for_best_model="accuracy"

    )

    # Initialize Trainer with the custom metric function
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
        compute_metrics=compute_metrics
    )

    # Train the model and log loss
    trainer.train()

    # Evaluate the model and return accuracy results for each epoch
    eval_results = trainer.evaluate()
    return eval_results, trainer

# Dictionary to store evaluation results for each model
model_results = {}

# Define models and tokenizers
models = {
    "BERT": (BertTokenizer, BertForSequenceClassification, 'bert-base-uncased'),
    "RoBERTa": (RobertaTokenizer, RobertaForSequenceClassification, 'roberta-base'),
    "DistilBERT": (DistilBertTokenizer, DistilBertForSequenceClassification, 'distilbert-base-uncased'),
    "XLNet": (XLNetTokenizer, XLNetForSequenceClassification, 'xlnet-base-cased'),
    "ALBERT": (AlbertTokenizer, AlbertForSequenceClassification, 'albert-base-v2')
}

# Dataframe to store final epoch metrics for each model
final_results_df = pd.DataFrame(columns=["Model", "Accuracy", "Precision", "Recall", "F1"])

# Train and evaluate each model
for model_name, (tokenizer_class, model_class, model_pretrained) in models.items():
    print(f"Training {model_name} model...")
    eval_results, trainer = train_model(model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels, label_encoder)
    model_results[model_name] = eval_results

    # Extract metrics from the final epoch and store in summary DataFrame
    final_metrics = {
        "Model": model_name,
        "Accuracy": eval_results["eval_accuracy"],
        "Precision": eval_results["eval_precision"],
        "Recall": eval_results["eval_recall"],
        "F1": eval_results["eval_f1"]
    }
    # final_results_df = final_results_df.append(final_metrics, ignore_index=True)
    final_results_df = pd.concat([final_results_df, pd.DataFrame([final_metrics])], ignore_index=True)
    print(f"Finished training {model_name}.")
    print(f"Evaluation Results for {model_name}: {eval_results}")

# Sort results by Accuracy and F1 Score in descending order
final_results_accuracy = final_results_df.sort_values(by="Accuracy", ascending=False).reset_index(drop=True)
final_results_f1 = final_results_df.sort_values(by="F1", ascending=False).reset_index(drop=True)

# Display the sorted tables
print("\nFinal Results Sorted by Accuracy:\n", final_results_accuracy)
print("\nFinal Results Sorted by F1 Score:\n", final_results_f1)

Training BERT model...

tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]

vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]

tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]

config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]

model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]

Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
wandb: WARNING The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.
wandb: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.

wandb: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)
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wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:

 ··········

wandb: Appending key for api.wandb.ai to your netrc file: /root/.netrc

# Final Results Sorted by Accuracy
final_results_accuracy

# Final Results Sorted by F1 score
final_results_f1

from transformers import TrainingArguments, Trainer
from datasets import Dataset
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
import pandas as pd
import warnings
import torch

warnings.filterwarnings("ignore")

# Function to compute metrics
def compute_metrics(pred):
    labels = pred.label_ids
    preds = pred.predictions.argmax(-1)
    accuracy = accuracy_score(labels, preds)
    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')
    return {
        "accuracy": accuracy,
        "precision": precision,
        "recall": recall,
        "f1": f1
    }

# Initialize a list to store results for each model
model_results = []

# Training function for each model
def train_and_evaluate_model(model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels):
    tokenizer = tokenizer_class.from_pretrained(model_pretrained)
    model = model_class.from_pretrained(model_pretrained, num_labels=len(set(train_labels)))

    # Move all model parameters to contiguous memory if necessary
    for param in model.parameters():
        if not param.is_contiguous():
            param.data = param.data.contiguous()

    # Tokenize the datasets
    train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=512)
    test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=512)

    # Convert tokenized data to Dataset format
    train_dataset = Dataset.from_dict({
        "input_ids": train_encodings["input_ids"],
        "attention_mask": train_encodings["attention_mask"],
        "labels": train_labels
    })
    test_dataset = Dataset.from_dict({
        "input_ids": test_encodings["input_ids"],
        "attention_mask": test_encodings["attention_mask"],
        "labels": test_labels
    })

    # Define training arguments
    training_args = TrainingArguments(
        output_dir='./results',
        num_train_epochs=10,
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        warmup_steps=500,
        weight_decay=0.01,
        logging_dir='./logs',
        logging_steps=10,
        evaluation_strategy="epoch"
    )

    # Define Trainer with custom training evaluation to get train metrics
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
        compute_metrics=compute_metrics
    )

    # Train and evaluate
    trainer.train()
    # Explicitly evaluate on training data for metrics
    train_metrics = trainer.evaluate(train_dataset)
    eval_metrics = trainer.evaluate(test_dataset)

    return train_metrics, eval_metrics

# Define models and tokenizers
models = {
    "BERT": (BertTokenizer, BertForSequenceClassification, 'bert-base-uncased'),
    "RoBERTa": (RobertaTokenizer, RobertaForSequenceClassification, 'roberta-base'),
    "DistilBERT": (DistilBertTokenizer, DistilBertForSequenceClassification, 'distilbert-base-uncased'),
    "XLNet": (XLNetTokenizer, XLNetForSequenceClassification, 'xlnet-base-cased'),
    "ALBERT": (AlbertTokenizer, AlbertForSequenceClassification, 'albert-base-v2')
}

# Loop through models
for model_name, (tokenizer_class, model_class, model_pretrained) in models.items():
    print(f"Training {model_name}...")
    train_metrics, eval_metrics = train_and_evaluate_model(model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels)

    # Append the last epoch's metrics for each model to model_results list
    model_results.append({
        "Model": model_name,
        "Train Accuracy": train_metrics.get("eval_accuracy", 0),
        "Validation Accuracy": eval_metrics.get("eval_accuracy", 0),
        "Train Precision": train_metrics.get("eval_precision", 0),
        "Validation Precision": eval_metrics.get("eval_precision", 0),
        "Train Recall": train_metrics.get("eval_recall", 0),
        "Validation Recall": eval_metrics.get("eval_recall", 0),
        "Train F1": train_metrics.get("eval_f1", 0),
        "Validation F1": eval_metrics.get("eval_f1", 0),
    })

# Convert results to DataFrame and display sorted tables
results_df = pd.DataFrame(model_results)

# Display summary tables
print("Summary Table - Ordered by Validation Accuracy (Descending):")
print(results_df.sort_values(by="Validation Accuracy", ascending=False).to_string(index=False))

print("\nSummary Table - Ordered by Validation F1 Score (Descending):")
print(results_df.sort_values(by="Validation F1", ascending=False).to_string(index=False))

Training BERT...

Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Training RoBERTa...

Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Training DistilBERT...

Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Training XLNet...

Some weights of XLNetForSequenceClassification were not initialized from the model checkpoint at xlnet-base-cased and are newly initialized: ['logits_proj.bias', 'logits_proj.weight', 'sequence_summary.summary.bias', 'sequence_summary.summary.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Training ALBERT...

Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

from transformers import TrainingArguments, Trainer, EarlyStoppingCallback
from datasets import Dataset
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
import pandas as pd
import warnings
import torch

warnings.filterwarnings("ignore")

# Function to compute metrics
def compute_metrics(pred):
    labels = pred.label_ids
    preds = pred.predictions.argmax(-1)
    accuracy = accuracy_score(labels, preds)
    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')
    return {
        "accuracy": accuracy,
        "precision": precision,
        "recall": recall,
        "f1": f1
    }

# Initialize a list to store results for each model
model_results = []

# Training function for each model
def train_and_evaluate_model(model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels):
    tokenizer = tokenizer_class.from_pretrained(model_pretrained)
    model = model_class.from_pretrained(model_pretrained, num_labels=len(set(train_labels)))

    # Ensure all model parameters are in contiguous memory if necessary
    for param in model.parameters():
        if not param.is_contiguous():
            param.data = param.data.contiguous()

    # Tokenize datasets
    train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=512)
    test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=512)

    # Convert tokenized data to Dataset format
    train_dataset = Dataset.from_dict({
        "input_ids": train_encodings["input_ids"],
        "attention_mask": train_encodings["attention_mask"],
        "labels": train_labels
    })
    test_dataset = Dataset.from_dict({
        "input_ids": test_encodings["input_ids"],
        "attention_mask": test_encodings["attention_mask"],
        "labels": test_labels
    })

    # Define training arguments to optimize for F1
    training_args = TrainingArguments(
        output_dir='./results',
        num_train_epochs=5,
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        evaluation_strategy="epoch",
        save_strategy="epoch",
        load_best_model_at_end=True,
        metric_for_best_model="f1", # optimizing and picking the models for best f-1 score
        greater_is_better=True,
        warmup_steps=500,
        weight_decay=0.01,
        logging_dir='./logs',
        logging_steps=10,
        report_to="none"
    )

    # Initialize the Trainer with EarlyStoppingCallback
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
        compute_metrics=compute_metrics,
        callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
    )

    # Train and evaluate
    trainer.train()
    # Evaluate for training and validation metrics
    train_metrics = trainer.evaluate(train_dataset)
    eval_metrics = trainer.evaluate(test_dataset)

    return train_metrics, eval_metrics

# Define models and tokenizers
models = {
    "BERT": (BertTokenizer, BertForSequenceClassification, 'bert-base-uncased'),
    "RoBERTa": (RobertaTokenizer, RobertaForSequenceClassification, 'roberta-base'),
    "DistilBERT": (DistilBertTokenizer, DistilBertForSequenceClassification, 'distilbert-base-uncased'),
    "XLNet": (XLNetTokenizer, XLNetForSequenceClassification, 'xlnet-base-cased'),
    "ALBERT": (AlbertTokenizer, AlbertForSequenceClassification, 'albert-base-v2')
}

# Loop through models and collect results
for model_name, (tokenizer_class, model_class, model_pretrained) in models.items():
    print(f"Training {model_name}...")
    train_metrics, eval_metrics = train_and_evaluate_model(model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels)

    # Append the last epoch's metrics for each model to model_results list
    model_results.append({
        "Model": model_name,
        "Train Accuracy": train_metrics.get("eval_accuracy", 0),
        "Validation Accuracy": eval_metrics.get("eval_accuracy", 0),
        "Train Precision": train_metrics.get("eval_precision", 0),
        "Validation Precision": eval_metrics.get("eval_precision", 0),
        "Train Recall": train_metrics.get("eval_recall", 0),
        "Validation Recall": eval_metrics.get("eval_recall", 0),
        "Train F1": train_metrics.get("eval_f1", 0),
        "Validation F1": eval_metrics.get("eval_f1", 0),
    })

# Convert results to DataFrame and display sorted tables
results_df = pd.DataFrame(model_results)

# Display summary tables
print("Summary Table - Ordered by Validation Accuracy (Descending):")
print(results_df.sort_values(by="Validation Accuracy", ascending=False).to_string(index=False))

print("\nSummary Table - Ordered by Validation F1 Score (Descending):")
print(results_df.sort_values(by="Validation F1", ascending=False).to_string(index=False))

Training BERT...

Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Training RoBERTa...

Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Training DistilBERT...

Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Training XLNet...

Some weights of XLNetForSequenceClassification were not initialized from the model checkpoint at xlnet-base-cased and are newly initialized: ['logits_proj.bias', 'logits_proj.weight', 'sequence_summary.summary.bias', 'sequence_summary.summary.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Training ALBERT...

Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

# Install required libraries
!pip install transformers datasets torch scikit-learn imbalanced-learn

from transformers import (
    BertTokenizer, BertForSequenceClassification,
    RobertaTokenizer, RobertaForSequenceClassification,
    DistilBertTokenizer, DistilBertForSequenceClassification,
    XLNetTokenizer, XLNetForSequenceClassification,
    AlbertTokenizer, AlbertForSequenceClassification,
    Trainer, TrainingArguments, EarlyStoppingCallback
)
from datasets import Dataset
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import RandomOverSampler
import pandas as pd
import warnings
import torch

# Suppress all warnings
warnings.filterwarnings("ignore")

# Load data
df = data_original.copy()
df = df[['Description', 'Accident Level']]  # Columns with text and labels

# Encode labels to numeric format
label_encoder = LabelEncoder()
df['labels'] = label_encoder.fit_transform(df['Accident Level'])

# Split data into training and testing sets
train_texts, test_texts, train_labels, test_labels = train_test_split(
    df['Description'].tolist(),
    df['labels'].tolist(),
    test_size=0.2,
    random_state=42,
    stratify=df['labels']  # Ensures proportional representation in splits
)

# Initialize RandomOverSampler
ros = RandomOverSampler(random_state=42)

# Reshape train_texts for oversampling (required by RandomOverSampler)
train_texts_df = pd.DataFrame(train_texts, columns=['Description'])

# Apply RandomOverSampler to training data
train_texts_resampled, train_labels_resampled = ros.fit_resample(train_texts_df, train_labels)

# Convert back to lists
train_texts_resampled = train_texts_resampled['Description'].tolist()

# Optional: Verify the class distribution after oversampling
from collections import Counter
print("Class distribution after oversampling:", Counter(train_labels_resampled))

# Function to compute metrics
def compute_metrics(pred):
    labels = pred.label_ids
    preds = pred.predictions.argmax(-1)
    accuracy = accuracy_score(labels, preds)
    precision, recall, f1, _ = precision_recall_fscore_support(
        labels, preds, average='weighted'
    )
    return {
        "accuracy": accuracy,
        "precision": precision,
        "recall": recall,
        "f1": f1
    }

# Training and evaluation function
def train_and_evaluate_model(model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels):
    # Initialize tokenizer and model
    tokenizer = tokenizer_class.from_pretrained(model_pretrained)
    model = model_class.from_pretrained(model_pretrained, num_labels=len(set(train_labels)))

    # Ensure all model parameters are contiguous in memory
    for param in model.parameters():
        if not param.is_contiguous():
            param.data = param.data.contiguous()

    # Tokenize the training and testing data
    train_encodings = tokenizer(
        train_texts, truncation=True, padding=True, max_length=512
    )
    test_encodings = tokenizer(
        test_texts, truncation=True, padding=True, max_length=512
    )

    # Convert tokenized data to Hugging Face Dataset format
    train_dataset = Dataset.from_dict({
        "input_ids": train_encodings["input_ids"],
        "attention_mask": train_encodings["attention_mask"],
        "labels": train_labels
    })
    test_dataset = Dataset.from_dict({
        "input_ids": test_encodings["input_ids"],
        "attention_mask": test_encodings["attention_mask"],
        "labels": test_labels
    })

    # Define training arguments to optimize for F1 Score
    training_args = TrainingArguments(
        output_dir='./results',
        num_train_epochs=10,
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        evaluation_strategy="epoch",
        save_strategy="epoch",
        load_best_model_at_end=True,
        metric_for_best_model="f1",  # Optimize based on F1 score
        greater_is_better=True,
        warmup_steps=500,
        weight_decay=0.01,
        logging_dir='./logs',
        logging_steps=10,
        report_to="none"  # Disable reporting to external services
    )

    # Initialize Trainer with EarlyStoppingCallback
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
        compute_metrics=compute_metrics,
        callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
    )

    # Train the model
    trainer.train()

    # Evaluate on training data
    train_metrics = trainer.evaluate(train_dataset)

    # Evaluate on validation/test data
    eval_metrics = trainer.evaluate(test_dataset)

    # Compute predictions on the train set for classification report
    train_predictions = trainer.predict(train_dataset)
    train_preds = train_predictions.predictions.argmax(-1)
    train_report = classification_report(train_labels, train_preds, target_names=label_encoder.classes_)

    # Compute predictions on the test set for classification report
    test_predictions = trainer.predict(test_dataset)
    test_preds = test_predictions.predictions.argmax(-1)
    test_report = classification_report(test_labels, test_preds, target_names=label_encoder.classes_)

    return train_metrics, eval_metrics, train_report, test_report

# Define models and tokenizers
models = {
    "BERT": (BertTokenizer, BertForSequenceClassification, 'bert-base-uncased'),
    "RoBERTa": (RobertaTokenizer, RobertaForSequenceClassification, 'roberta-base'),
    "DistilBERT": (DistilBertTokenizer, DistilBertForSequenceClassification, 'distilbert-base-uncased'),
    "XLNet": (XLNetTokenizer, XLNetForSequenceClassification, 'xlnet-base-cased'),
    "ALBERT": (AlbertTokenizer, AlbertForSequenceClassification, 'albert-base-v2')
}

# Initialize a list to store results for each model
model_results = []

# Loop through each model, train, evaluate, and collect metrics
for model_name, (tokenizer_class, model_class, model_pretrained) in models.items():
    print(f"Training {model_name}...")
    train_metrics, eval_metrics, train_report, test_report = train_and_evaluate_model(
        model_pretrained,
        tokenizer_class,
        model_class,
        train_texts_resampled,
        train_labels_resampled,
        test_texts,
        test_labels
    )

    # Append metrics to the results list
    model_results.append({
        "Model": model_name,
        "Train Accuracy": train_metrics.get("eval_accuracy", 0),
        "Test Accuracy": eval_metrics.get("eval_accuracy", 0),
        "Train Precision": train_metrics.get("eval_precision", 0),
        "Test Precision": eval_metrics.get("eval_precision", 0),
        "Train Recall": train_metrics.get("eval_recall", 0),
        "Test Recall": eval_metrics.get("eval_recall", 0),
        "Train F1": train_metrics.get("eval_f1", 0),
        "Test F1": eval_metrics.get("eval_f1", 0),
    })

    print(f"Finished training {model_name}.")
    print(f"Train Metrics for {model_name}: {train_metrics}")
    print(f"Validation Metrics for {model_name}: {eval_metrics}")
    print(f"\nTrain Classification Report for {model_name}:\n{train_report}")
    print(f"\nTest Classification Report for {model_name}:\n{test_report}\n")

# Convert results to a DataFrame
results_df = pd.DataFrame(model_results)

# Display summary tables sorted by Validation Accuracy and Validation F1 Score
print("Summary Table - Ordered by Validation Accuracy (Descending):")
print(results_df.sort_values(by="Test Accuracy", ascending=False).to_string(index=False))

print("\nSummary Table - Ordered by Validation F1 Score (Descending):")
print(results_df.sort_values(by="Test F1", ascending=False).to_string(index=False))

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Class distribution after oversampling: Counter({0: 253, 3: 253, 1: 253, 4: 253, 2: 253})
Training BERT...

Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Finished training BERT.
Train Metrics for BERT: {'eval_loss': 0.015103112906217575, 'eval_accuracy': 0.9944664031620554, 'eval_precision': 0.9946153846153846, 'eval_recall': 0.9944664031620554, 'eval_f1': 0.994465343943247, 'eval_runtime': 4.4745, 'eval_samples_per_second': 282.715, 'eval_steps_per_second': 35.535, 'epoch': 9.0}
Validation Metrics for BERT: {'eval_loss': 2.1611242294311523, 'eval_accuracy': 0.7294117647058823, 'eval_precision': 0.5536498936924167, 'eval_recall': 0.7294117647058823, 'eval_f1': 0.6294923448831587, 'eval_runtime': 0.3177, 'eval_samples_per_second': 267.518, 'eval_steps_per_second': 34.62, 'epoch': 9.0}

Train Classification Report for BERT:
              precision    recall  f1-score   support

           I       1.00      1.00      1.00       253
          II       1.00      0.97      0.99       253
         III       1.00      1.00      1.00       253
          IV       0.97      1.00      0.99       253
           V       1.00      1.00      1.00       253

    accuracy                           0.99      1265
   macro avg       0.99      0.99      0.99      1265
weighted avg       0.99      0.99      0.99      1265


Test Classification Report for BERT:
              precision    recall  f1-score   support

           I       0.75      0.98      0.85        63
          II       0.00      0.00      0.00         8
         III       0.00      0.00      0.00         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.73        85
   macro avg       0.15      0.20      0.17        85
weighted avg       0.55      0.73      0.63        85


Training RoBERTa...

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Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

!pip install transformers datasets torch scikit-learn imbalanced-learn

# Load data
df = data_original.copy()
df = df[['Description', 'Accident Level']]  # Columns with text and labels

# Calculate value counts and percentages for the 'Accident Level' column in original dataset
value_counts = df['Accident Level'].value_counts()
percentages = df['Accident Level'].value_counts(normalize=True) * 100

# Plot the bar chart
plt.figure(figsize=(10, 6))
value_counts.plot(kind='bar', color='skyblue')
plt.title('Value Counts and Percentages for Accident Level Column')
plt.ylabel('Count')
plt.xlabel('Accident Level')

# Show the counts and percentages on top of the bars
for i, (count, pct) in enumerate(zip(value_counts, percentages)):
    plt.text(i, count + 0.5, f"{count} ({pct:.2f}%)", ha='center', fontweight='bold')

plt.show()

df.head(2)

# Map each accident level to the corresponding severity level
def map_severity(level):
    if level == 'I':
        return 'Low'
    elif level in ['II', 'III']:
        return 'Medium'
    elif level in ['IV', 'V']:
        return 'High'

# Apply the mapping function to create a new column
df['Accident Severity'] = df['Accident Level'].apply(map_severity)

# Display the updated DataFrame
df.head()

from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
# Encode labels
label_encoder = LabelEncoder()
df['labels'] = label_encoder.fit_transform(df['Accident Severity'])

# Split data into train and test
train_texts, test_texts, train_labels, test_labels = train_test_split(
    df['Description'].tolist(), df['labels'].tolist(), test_size=0.2, random_state=42
)

df.head()

# Calculate value counts and percentages for the 'Accident Level' column in the dataset containing the merged accident class levels.
value_counts = df['Accident Severity'].value_counts()
percentages = df['Accident Severity'].value_counts(normalize=True) * 100

# Plot the bar chart
plt.figure(figsize=(10, 6))
value_counts.plot(kind='bar', color='skyblue')
plt.title('Value Counts and Percentages for Accident Severity Column')
plt.ylabel('Count')
plt.xlabel('Accident Severity')

# Show the counts and percentages on top of the bars
for i, (count, pct) in enumerate(zip(value_counts, percentages)):
    plt.text(i, count + 0.5, f"{count} ({pct:.2f}%)", ha='center', fontweight='bold')

plt.show()

from transformers import TrainingArguments, Trainer
from datasets import Dataset
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, classification_report
import pandas as pd
import warnings
import torch

warnings.filterwarnings("ignore")

# Function to compute metrics
def compute_metrics(pred):
    labels = pred.label_ids
    preds = pred.predictions.argmax(-1)
    accuracy = accuracy_score(labels, preds)
    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')
    return {
        "accuracy": accuracy,
        "precision": precision,
        "recall": recall,
        "f1": f1
    }

# Function to generate a classification report
def get_classification_report(labels, preds):
    return classification_report(labels, preds, output_dict=True)

# Training function for each model
def train_and_evaluate_model(model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels):
    tokenizer = tokenizer_class.from_pretrained(model_pretrained)
    model = model_class.from_pretrained(model_pretrained, num_labels=len(set(train_labels)))

    # Ensure all model parameters are contiguous
    for param in model.parameters():
        if not param.is_contiguous():
            param.data = param.data.contiguous()

    # Tokenize the datasets
    train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=512)
    test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=512)

    # Convert tokenized data to Dataset format
    train_dataset = Dataset.from_dict({
        "input_ids": train_encodings["input_ids"],
        "attention_mask": train_encodings["attention_mask"],
        "labels": train_labels
    })
    test_dataset = Dataset.from_dict({
        "input_ids": test_encodings["input_ids"],
        "attention_mask": test_encodings["attention_mask"],
        "labels": test_labels
    })

    # Define training arguments
    training_args = TrainingArguments(
        output_dir='./results',
        num_train_epochs=10,
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        warmup_steps=500,
        weight_decay=0.01,
        logging_dir='./logs',
        logging_steps=10,
        evaluation_strategy="epoch",
        load_best_model_at_end=True,
        save_strategy="epoch",
        metric_for_best_model="f1",
        greater_is_better=True,
    )

    # Define Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
        compute_metrics=compute_metrics
    )

    # Train and evaluate
    trainer.train()

    # Get final predictions and generate classification reports
    train_preds = trainer.predict(train_dataset)
    test_preds = trainer.predict(test_dataset)

    train_report = get_classification_report(train_preds.label_ids, train_preds.predictions.argmax(-1))
    test_report = get_classification_report(test_preds.label_ids, test_preds.predictions.argmax(-1))

    # Get accuracy and f1 metrics for summary table
    train_accuracy = train_report["accuracy"]
    test_accuracy = test_report["accuracy"]
    train_f1 = train_report["weighted avg"]["f1-score"]
    test_f1 = test_report["weighted avg"]["f1-score"]

    return train_report, test_report, train_accuracy, test_accuracy, train_f1, test_f1

# Define models and tokenizers
models = {
    "BERT": (BertTokenizer, BertForSequenceClassification, 'bert-base-uncased'),
    "RoBERTa": (RobertaTokenizer, RobertaForSequenceClassification, 'roberta-base'),
    "DistilBERT": (DistilBertTokenizer, DistilBertForSequenceClassification, 'distilbert-base-uncased'),
    "XLNet": (XLNetTokenizer, XLNetForSequenceClassification, 'xlnet-base-cased'),
    "ALBERT": (AlbertTokenizer, AlbertForSequenceClassification, 'albert-base-v2')
}

# Initialize a list to store results for each model
model_results = []

# Loop through models
for model_name, (tokenizer_class, model_class, model_pretrained) in models.items():
    print(f"Training {model_name}...")
    train_report, test_report, train_accuracy, test_accuracy, train_f1, test_f1 = train_and_evaluate_model(
        model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels
    )

    # Append the best epoch's classification report and metrics for each model to model_results list
    model_results.append({
        "Model": model_name,
        "Train Accuracy": train_accuracy,
        "Test Accuracy": test_accuracy,
        "Train F1": train_f1,
        "Test F1": test_f1,
        "Train Classification Report": train_report,
        "Test Classification Report": test_report
    })

    print(f"Finished training {model_name}.\n")
    print(f"Train Classification Report for {model_name}:\n", pd.DataFrame(train_report).T)
    print(f"Test Classification Report for {model_name}:\n", pd.DataFrame(test_report).T)

# Convert results to DataFrame for summary table
summary_df = pd.DataFrame(model_results)[["Model", "Train Accuracy", "Test Accuracy", "Train F1", "Test F1"]]

# Display summary tables ordered by Test Accuracy and Test F1
print("\nSummary Table - Ordered by Test Accuracy (Descending):")
print(summary_df.sort_values(by="Test Accuracy", ascending=False).to_string(index=False))

print("\nSummary Table - Ordered by Test F1 Score (Descending):")
print(summary_df.sort_values(by="Test F1", ascending=False).to_string(index=False))

Training BERT...

Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Finished training BERT.

Train Classification Report for BERT:
               precision    recall  f1-score     support
0              0.966667  0.906250  0.935484   32.000000
1              0.980237  1.000000  0.990020  248.000000
2              0.964912  0.916667  0.940171   60.000000
accuracy       0.976471  0.976471  0.976471    0.976471
macro avg      0.970605  0.940972  0.955225  340.000000
weighted avg   0.976256  0.976471  0.976090  340.000000
Test Classification Report for BERT:
               precision    recall  f1-score  support
0              0.000000  0.000000  0.000000      6.0
1              0.825000  0.970588  0.891892     68.0
2              0.500000  0.181818  0.266667     11.0
accuracy       0.800000  0.800000  0.800000      0.8
macro avg      0.441667  0.384135  0.386186     85.0
weighted avg   0.724706  0.800000  0.748023     85.0
Training RoBERTa...

Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Finished training RoBERTa.

Train Classification Report for RoBERTa:
               precision    recall  f1-score     support
0              1.000000  0.031250  0.060606   32.000000
1              0.814570  0.991935  0.894545  248.000000
2              0.702703  0.433333  0.536082   60.000000
accuracy       0.802941  0.802941  0.802941    0.802941
macro avg      0.839091  0.485506  0.497078  340.000000
weighted avg   0.812281  0.802941  0.752799  340.000000
Test Classification Report for RoBERTa:
               precision    recall  f1-score  support
0              0.000000  0.000000  0.000000      6.0
1              0.807229  0.985294  0.887417     68.0
2              0.500000  0.090909  0.153846     11.0
accuracy       0.800000  0.800000  0.800000      0.8
macro avg      0.435743  0.358734  0.347088     85.0
weighted avg   0.710489  0.800000  0.729843     85.0
Training DistilBERT...

Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Finished training DistilBERT.

Train Classification Report for DistilBERT:
               precision    recall  f1-score     support
0              0.909091  0.625000  0.740741   32.000000
1              0.953668  0.995968  0.974359  248.000000
2              0.830508  0.816667  0.823529   60.000000
accuracy       0.929412  0.929412  0.929412    0.929412
macro avg      0.897756  0.812545  0.846210  340.000000
weighted avg   0.927738  0.929412  0.925754  340.000000
Test Classification Report for DistilBERT:
               precision    recall  f1-score    support
0              0.000000  0.000000  0.000000   6.000000
1              0.826667  0.911765  0.867133  68.000000
2              0.222222  0.181818  0.200000  11.000000
accuracy       0.752941  0.752941  0.752941   0.752941
macro avg      0.349630  0.364528  0.355711  85.000000
weighted avg   0.690092  0.752941  0.719589  85.000000
Training XLNet...

Some weights of XLNetForSequenceClassification were not initialized from the model checkpoint at xlnet-base-cased and are newly initialized: ['logits_proj.bias', 'logits_proj.weight', 'sequence_summary.summary.bias', 'sequence_summary.summary.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Finished training XLNet.

Train Classification Report for XLNet:
               precision    recall  f1-score     support
0              0.812500  0.812500  0.812500   32.000000
1              0.959677  0.959677  0.959677  248.000000
2              0.833333  0.833333  0.833333   60.000000
accuracy       0.923529  0.923529  0.923529    0.923529
macro avg      0.868504  0.868504  0.868504  340.000000
weighted avg   0.923529  0.923529  0.923529  340.000000
Test Classification Report for XLNet:
               precision    recall  f1-score    support
0              0.400000  0.333333  0.363636   6.000000
1              0.842857  0.867647  0.855072  68.000000
2              0.300000  0.272727  0.285714  11.000000
accuracy       0.752941  0.752941  0.752941   0.752941
macro avg      0.514286  0.491236  0.501474  85.000000
weighted avg   0.741345  0.752941  0.746701  85.000000
Training ALBERT...

Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

# Performance metrics data sorted in descending order of test accuracy
summary_df.sort_values(by="Test Accuracy", ascending=False)

# Performance metrics data sorted in descending order of test F1 score
summary_df.sort_values(by="Test F1", ascending=False)

# Install required libraries
!pip install transformers datasets torch scikit-learn imbalanced-learn

from transformers import (
    BertTokenizer, BertForSequenceClassification,
    RobertaTokenizer, RobertaForSequenceClassification,
    DistilBertTokenizer, DistilBertForSequenceClassification,
    XLNetTokenizer, XLNetForSequenceClassification,
    AlbertTokenizer, AlbertForSequenceClassification,
    Trainer, TrainingArguments, EarlyStoppingCallback
)
from datasets import Dataset
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import SMOTE
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import warnings
import torch

# Suppress all warnings
warnings.filterwarnings("ignore")

# Load data
df = data_original.copy()
df = df[['Description', 'Accident Level']]  # Columns with text and labels

# Map each accident level to the corresponding severity level
def map_severity(level):
    if level == 'I':
        return 'Low'
    elif level in ['II', 'III']:
        return 'Medium'
    elif level in ['IV', 'V']:
        return 'High'

# Apply the mapping function to create a new column
df['Accident Severity'] = df['Accident Level'].apply(map_severity)

# Encode labels to numeric format
label_encoder = LabelEncoder()
df['labels'] = label_encoder.fit_transform(df['Accident Severity'])

# Split data into training and testing sets
train_texts, test_texts, train_labels, test_labels = train_test_split(
    df['Description'].tolist(),
    df['labels'].tolist(),
    test_size=0.2,
    random_state=42,
    stratify=df['labels']  # Ensures proportional representation in splits
)

# Initialize SMOTE
smote = SMOTE(random_state=42)

# Step 1: Transform text data using TF-IDF
tfidf_vectorizer = TfidfVectorizer(max_features=1000)  # Adjust max_features as needed
train_tfidf = tfidf_vectorizer.fit_transform(train_texts).toarray()
test_tfidf = tfidf_vectorizer.transform(test_texts).toarray()

# Step 2: Apply SMOTE
smote = SMOTE(random_state=42)
train_tfidf_resampled, train_labels_resampled = smote.fit_resample(train_tfidf, train_labels)

# Step 3: Map back to original text format if required
# Convert TF-IDF back into readable data, if needed (e.g., to debug or understand results)
# Here, we'll directly use `train_tfidf_resampled` and `train_labels_resampled` for model training

# Optional: Verify the class distribution after oversampling
from collections import Counter
print("Class distribution after SMOTE:", Counter(train_labels_resampled))

# Function to compute metrics
def compute_metrics(pred):
    labels = pred.label_ids
    preds = pred.predictions.argmax(-1)
    accuracy = accuracy_score(labels, preds)
    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')
    return {
        "accuracy": accuracy,
        "precision": precision,
        "recall": recall,
        "f1": f1
    }

# Function to generate a classification report
def get_classification_report(labels, preds):
    return classification_report(labels, preds, output_dict=True)

# Training function for each model
def train_and_evaluate_model(model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels):
    tokenizer = tokenizer_class.from_pretrained(model_pretrained)
    model = model_class.from_pretrained(model_pretrained, num_labels=len(set(train_labels)))

    # Ensure all model parameters are contiguous
    for param in model.parameters():
        if not param.is_contiguous():
            param.data = param.data.contiguous()

    # Tokenize the datasets
    train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=512)
    test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=512)

    # Convert tokenized data to Dataset format
    train_dataset = Dataset.from_dict({
        "input_ids": train_encodings["input_ids"],
        "attention_mask": train_encodings["attention_mask"],
        "labels": train_labels
    })
    test_dataset = Dataset.from_dict({
        "input_ids": test_encodings["input_ids"],
        "attention_mask": test_encodings["attention_mask"],
        "labels": test_labels
    })

    # Define training arguments
    training_args = TrainingArguments(
        output_dir='./results',
        num_train_epochs=10,
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        warmup_steps=500,
        weight_decay=0.01,
        logging_dir='./logs',
        logging_steps=10,
        evaluation_strategy="epoch",
        load_best_model_at_end=True,
        save_strategy="epoch",
        metric_for_best_model="f1",
        greater_is_better=True,
    )

    # Define Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
        compute_metrics=compute_metrics
    )

    # Train and evaluate
    trainer.train()

    # Get final predictions and generate classification reports
    train_preds = trainer.predict(train_dataset)
    test_preds = trainer.predict(test_dataset)

    train_report = get_classification_report(train_preds.label_ids, train_preds.predictions.argmax(-1))
    test_report = get_classification_report(test_preds.label_ids, test_preds.predictions.argmax(-1))

    # Get accuracy and f1 metrics for summary table
    train_accuracy = train_report["accuracy"]
    test_accuracy = test_report["accuracy"]
    train_f1 = train_report["weighted avg"]["f1-score"]
    test_f1 = test_report["weighted avg"]["f1-score"]

    return train_report, test_report, train_accuracy, test_accuracy, train_f1, test_f1

# Define models and tokenizers
models = {
    "BERT": (BertTokenizer, BertForSequenceClassification, 'bert-base-uncased'),
    "RoBERTa": (RobertaTokenizer, RobertaForSequenceClassification, 'roberta-base'),
    "DistilBERT": (DistilBertTokenizer, DistilBertForSequenceClassification, 'distilbert-base-uncased'),
    "XLNet": (XLNetTokenizer, XLNetForSequenceClassification, 'xlnet-base-cased'),
    "ALBERT": (AlbertTokenizer, AlbertForSequenceClassification, 'albert-base-v2')
}

# Initialize a list to store results for each model
model_results = []

# Loop through models
for model_name, (tokenizer_class, model_class, model_pretrained) in models.items():
    print(f"Training {model_name}...")
    train_report, test_report, train_accuracy, test_accuracy, train_f1, test_f1 = train_and_evaluate_model(
        model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels
    )

    # Append the best epoch's classification report and metrics for each model to model_results list
    model_results.append({
        "Model": model_name,
        "Train Accuracy": train_accuracy,
        "Test Accuracy": test_accuracy,
        "Train F1": train_f1,
        "Test F1": test_f1,
        "Train Classification Report": train_report,
        "Test Classification Report": test_report
    })

    print(f"Finished training {model_name}.\n")
    print(f"Train Classification Report for {model_name}:\n", pd.DataFrame(train_report).T)
    print(f"Test Classification Report for {model_name}:\n", pd.DataFrame(test_report).T)

# Convert results to DataFrame for summary table
summary_df = pd.DataFrame(model_results)[["Model", "Train Accuracy", "Test Accuracy", "Train F1", "Test F1"]]

# Display summary tables ordered by Test Accuracy and Test F1
print("\nSummary Table - Ordered by Test Accuracy (Descending):")
print(summary_df.sort_values(by="Test Accuracy", ascending=False).to_string(index=False))

print("\nSummary Table - Ordered by Test F1 Score (Descending):")
print(summary_df.sort_values(by="Test F1", ascending=False).to_string(index=False))

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Class distribution after SMOTE: Counter({1: 253, 0: 253, 2: 253})
Training BERT...

Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Finished training BERT.

Train Classification Report for BERT:
               precision    recall  f1-score     support
0              1.000000  0.100000  0.181818   30.000000
1              0.897163  1.000000  0.945794  253.000000
2              0.836364  0.807018  0.821429   57.000000
accuracy       0.888235  0.888235  0.888235    0.888235
macro avg      0.911176  0.635673  0.649680  340.000000
weighted avg   0.896044  0.888235  0.857535  340.000000
Test Classification Report for BERT:
               precision    recall  f1-score    support
0              0.000000  0.000000  0.000000   8.000000
1              0.756098  0.984127  0.855172  63.000000
2              0.666667  0.142857  0.235294  14.000000
accuracy       0.752941  0.752941  0.752941   0.752941
macro avg      0.474255  0.375661  0.363489  85.000000
weighted avg   0.670206  0.752941  0.672588  85.000000
Training RoBERTa...

Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Finished training RoBERTa.

Train Classification Report for RoBERTa:
               precision    recall  f1-score     support
0              0.850000  0.566667  0.680000   30.000000
1              0.968750  0.980237  0.974460  253.000000
2              0.750000  0.842105  0.793388   57.000000
accuracy       0.920588  0.920588  0.920588    0.920588
macro avg      0.856250  0.796336  0.815949  340.000000
weighted avg   0.921599  0.920588  0.918122  340.000000
Test Classification Report for RoBERTa:
               precision    recall  f1-score    support
0              0.500000  0.125000  0.200000   8.000000
1              0.788732  0.888889  0.835821  63.000000
2              0.416667  0.357143  0.384615  14.000000
accuracy       0.729412  0.729412  0.729412   0.729412
macro avg      0.568466  0.457011  0.473479  85.000000
weighted avg   0.700276  0.729412  0.701663  85.000000
Training DistilBERT...

Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Finished training DistilBERT.

Train Classification Report for DistilBERT:
               precision    recall  f1-score     support
0              0.806452  0.833333  0.819672   30.000000
1              0.965385  0.992095  0.978558  253.000000
2              0.938776  0.807018  0.867925   57.000000
accuracy       0.947059  0.947059  0.947059    0.947059
macro avg      0.903537  0.877482  0.888718  340.000000
weighted avg   0.946900  0.947059  0.945991  340.000000
Test Classification Report for DistilBERT:
               precision    recall  f1-score    support
0              0.666667  0.250000  0.363636   8.000000
1              0.787500  1.000000  0.881119  63.000000
2              1.000000  0.142857  0.250000  14.000000
accuracy       0.788235  0.788235  0.788235   0.788235
macro avg      0.818056  0.464286  0.498252  85.000000
weighted avg   0.811127  0.788235  0.728466  85.000000
Training XLNet...

Some weights of XLNetForSequenceClassification were not initialized from the model checkpoint at xlnet-base-cased and are newly initialized: ['logits_proj.bias', 'logits_proj.weight', 'sequence_summary.summary.bias', 'sequence_summary.summary.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Finished training XLNet.

Train Classification Report for XLNet:
               precision    recall  f1-score     support
0              0.916667  0.733333  0.814815   30.000000
1              0.996047  0.996047  0.996047  253.000000
2              0.873016  0.964912  0.916667   57.000000
accuracy       0.967647  0.967647  0.967647    0.967647
macro avg      0.928577  0.898098  0.909176  340.000000
weighted avg   0.968417  0.967647  0.966748  340.000000
Test Classification Report for XLNet:
               precision    recall  f1-score    support
0              0.000000  0.000000  0.000000   8.000000
1              0.794118  0.857143  0.824427  63.000000
2              0.411765  0.500000  0.451613  14.000000
accuracy       0.717647  0.717647  0.717647   0.717647
macro avg      0.401961  0.452381  0.425347  85.000000
weighted avg   0.656401  0.717647  0.685430  85.000000
Training ALBERT...

Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

# Install required libraries
!pip install transformers datasets torch scikit-learn imbalanced-learn

from transformers import (
    BertTokenizer, BertForSequenceClassification,
    RobertaTokenizer, RobertaForSequenceClassification,
    DistilBertTokenizer, DistilBertForSequenceClassification,
    XLNetTokenizer, XLNetForSequenceClassification,
    AlbertTokenizer, AlbertForSequenceClassification,
    Trainer, TrainingArguments, EarlyStoppingCallback
)
from datasets import Dataset
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import RandomOverSampler
import pandas as pd
import warnings
import torch

# Suppress all warnings
warnings.filterwarnings("ignore")

# Load data
df = data_original.copy()
df = df[['Description', 'Accident Level']]  # Columns with text and labels

# Map each accident level to the corresponding severity level
def map_severity(level):
    if level == 'I':
        return 'Low'
    elif level in ['II', 'III']:
        return 'Medium'
    elif level in ['IV', 'V']:
        return 'High'

# Apply the mapping function to create a new column
df['Accident Severity'] = df['Accident Level'].apply(map_severity)

# Encode labels to numeric format
label_encoder = LabelEncoder()
df['labels'] = label_encoder.fit_transform(df['Accident Severity'])

# Split data into training and testing sets
train_texts, test_texts, train_labels, test_labels = train_test_split(
    df['Description'].tolist(),
    df['labels'].tolist(),
    test_size=0.2,
    random_state=42,
    stratify=df['labels']  # Ensures proportional representation in splits
)

# Initialize RandomOverSampler -----------------------------------------------------------
ros = RandomOverSampler(random_state=42)

# Reshape train_texts for oversampling (required by RandomOverSampler)
train_texts_df = pd.DataFrame(train_texts, columns=['Description'])

# Apply RandomOverSampler to training data
train_texts_resampled, train_labels_resampled = ros.fit_resample(train_texts_df, train_labels)

# Convert back to lists
train_texts_resampled = train_texts_resampled['Description'].tolist()

# Optional: Verify the class distribution after oversampling
from collections import Counter
print("Class distribution after oversampling:", Counter(train_labels_resampled))

# Function to compute metrics
def compute_metrics(pred):
    labels = pred.label_ids
    preds = pred.predictions.argmax(-1)
    accuracy = accuracy_score(labels, preds)
    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')
    return {
        "accuracy": accuracy,
        "precision": precision,
        "recall": recall,
        "f1": f1
    }

# Function to generate a classification report
def get_classification_report(labels, preds):
    return classification_report(labels, preds, output_dict=True)

# Training function for each model
def train_and_evaluate_model(model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels):
    tokenizer = tokenizer_class.from_pretrained(model_pretrained)
    model = model_class.from_pretrained(model_pretrained, num_labels=len(set(train_labels)))

    # Ensure all model parameters are contiguous
    for param in model.parameters():
        if not param.is_contiguous():
            param.data = param.data.contiguous()

    # Tokenize the datasets
    train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=512)
    test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=512)

    # Convert tokenized data to Dataset format
    train_dataset = Dataset.from_dict({
        "input_ids": train_encodings["input_ids"],
        "attention_mask": train_encodings["attention_mask"],
        "labels": train_labels
    })
    test_dataset = Dataset.from_dict({
        "input_ids": test_encodings["input_ids"],
        "attention_mask": test_encodings["attention_mask"],
        "labels": test_labels
    })

    # Define training arguments
    training_args = TrainingArguments(
        output_dir='./results',
        num_train_epochs=10,
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        warmup_steps=500,
        weight_decay=0.01,
        logging_dir='./logs',
        logging_steps=10,
        evaluation_strategy="epoch",
        load_best_model_at_end=True,
        save_strategy="epoch",
        metric_for_best_model="f1",
        greater_is_better=True,
    )

    # Define Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
        compute_metrics=compute_metrics
    )

    # Train and evaluate
    trainer.train()

    # Get final predictions and generate classification reports
    train_preds = trainer.predict(train_dataset)
    test_preds = trainer.predict(test_dataset)

    train_report = get_classification_report(train_preds.label_ids, train_preds.predictions.argmax(-1))
    test_report = get_classification_report(test_preds.label_ids, test_preds.predictions.argmax(-1))

    # Get accuracy and f1 metrics for summary table
    train_accuracy = train_report["accuracy"]
    test_accuracy = test_report["accuracy"]
    train_f1 = train_report["weighted avg"]["f1-score"]
    test_f1 = test_report["weighted avg"]["f1-score"]

    return train_report, test_report, train_accuracy, test_accuracy, train_f1, test_f1

# Define models and tokenizers
models = {
    "BERT": (BertTokenizer, BertForSequenceClassification, 'bert-base-uncased'),
    "RoBERTa": (RobertaTokenizer, RobertaForSequenceClassification, 'roberta-base'),
    "DistilBERT": (DistilBertTokenizer, DistilBertForSequenceClassification, 'distilbert-base-uncased'),
    "XLNet": (XLNetTokenizer, XLNetForSequenceClassification, 'xlnet-base-cased'),
    "ALBERT": (AlbertTokenizer, AlbertForSequenceClassification, 'albert-base-v2')
}

# Initialize a list to store results for each model
model_results = []

# Loop through models
for model_name, (tokenizer_class, model_class, model_pretrained) in models.items():
    print(f"Training {model_name}...")
    train_report, test_report, train_accuracy, test_accuracy, train_f1, test_f1 = train_and_evaluate_model(
        model_pretrained, tokenizer_class, model_class, train_texts, train_labels, test_texts, test_labels
    )

    # Append the best epoch's classification report and metrics for each model to model_results list
    model_results.append({
        "Model": model_name,
        "Train Accuracy": train_accuracy,
        "Test Accuracy": test_accuracy,
        "Train F1": train_f1,
        "Test F1": test_f1,
        "Train Classification Report": train_report,
        "Test Classification Report": test_report
    })

    print(f"Finished training {model_name}.\n")
    print(f"Train Classification Report for {model_name}:\n", pd.DataFrame(train_report).T)
    print(f"Test Classification Report for {model_name}:\n", pd.DataFrame(test_report).T)

# Convert results to DataFrame for summary table
summary_df = pd.DataFrame(model_results)[["Model", "Train Accuracy", "Test Accuracy", "Train F1", "Test F1"]]

# Display summary tables ordered by Test Accuracy and Test F1
print("\nSummary Table - Ordered by Test Accuracy (Descending):")
print(summary_df.sort_values(by="Test Accuracy", ascending=False).to_string(index=False))

print("\nSummary Table - Ordered by Test F1 Score (Descending):")
print(summary_df.sort_values(by="Test F1", ascending=False).to_string(index=False))

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Class distribution after oversampling: Counter({1: 253, 0: 253, 2: 253})
Training BERT...

Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Finished training BERT.

Train Classification Report for BERT:
               precision    recall  f1-score     support
0              0.933333  0.933333  0.933333   30.000000
1              0.992157  1.000000  0.996063  253.000000
2              0.981818  0.947368  0.964286   57.000000
accuracy       0.985294  0.985294  0.985294    0.985294
macro avg      0.969103  0.960234  0.964561  340.000000
weighted avg   0.985233  0.985294  0.985201  340.000000
Test Classification Report for BERT:
               precision    recall  f1-score    support
0              0.200000  0.125000  0.153846   8.000000
1              0.777778  0.888889  0.829630  63.000000
2              0.250000  0.142857  0.181818  14.000000
accuracy       0.694118  0.694118  0.694118   0.694118
macro avg      0.409259  0.385582  0.388431  85.000000
weighted avg   0.636471  0.694118  0.659328  85.000000
Training RoBERTa...

Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Finished training RoBERTa.

Train Classification Report for RoBERTa:
               precision    recall  f1-score     support
0              0.958333  0.766667  0.851852   30.000000
1              0.988189  0.992095  0.990138  253.000000
2              0.870968  0.947368  0.907563   57.000000
accuracy       0.964706  0.964706  0.964706    0.964706
macro avg      0.939163  0.902043  0.916518  340.000000
weighted avg   0.965903  0.964706  0.964093  340.000000
Test Classification Report for RoBERTa:
               precision    recall  f1-score    support
0              0.666667  0.250000  0.363636   8.000000
1              0.842857  0.936508  0.887218  63.000000
2              0.500000  0.428571  0.461538  14.000000
accuracy       0.788235  0.788235  0.788235   0.788235
macro avg      0.669841  0.538360  0.570798  85.000000
weighted avg   0.769804  0.788235  0.767828  85.000000
Training DistilBERT...

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	Date	Country	City	Industry Sector	Accident Level	Potential Accident Level	Gender	Employee type	Critical Risk	Description
77	2016-04-01	Country_01	Local_01	Mining	I	V	Male	Third Party (Remote)	Others	In circumstances that two workers of the Abrat...
262	2016-12-01	Country_01	Local_03	Mining	I	IV	Male	Employee	Others	During the activity of chuteo of ore in hopper...
303	2017-01-21	Country_02	Local_02	Mining	I	I	Male	Third Party (Remote)	Others	Employees engaged in the removal of material f...
345	2017-03-02	Country_03	Local_10	Others	I	I	Male	Third Party	Venomous Animals	On 02/03/17 during the soil sampling in the re...
346	2017-03-02	Country_03	Local_10	Others	I	I	Male	Third Party	Venomous Animals	On 02/03/17 during the soil sampling in the re...
355	2017-03-15	Country_03	Local_10	Others	I	I	Male	Third Party	Venomous Animals	Team of the VMS Project performed soil collect...
397	2017-05-23	Country_01	Local_04	Mining	I	IV	Male	Third Party	Projection of fragments	In moments when the 02 collaborators carried o...

	Description	Preprocessed_Description
0	While removing the drill rod of the Jumbo 08 f...	removing drill rod jumbo maintenance superviso...
1	During the activation of a sodium sulphide pum...	activation sodium sulphide pump piping uncoupl...
2	In the sub-station MILPO located at level +170...	substation milpo located level collaborator ex...
3	Being 9:45 am. approximately in the Nv. 1880 C...	approximately nv cx695 ob7 personnel begin tas...
4	Approximately at 11:45 a.m. in circumstances t...	approximately circumstance mechanic anthony gr...

Aspect	BERT Embeddings + Classifiers	Fine-Tuned Transformers
Contextual Understanding	Limited to static embeddings	Dynamic, task-specific contextualization
Handling Sequential Data	Poor	Excellent (self-attention)
Adaptation to Specific Data	None	High
Imbalanced Data Handling	Difficult	Better with weighted loss or sampling
Training Efficiency	Faster	Computationally intensive but optimized
Prediction Quality	Decoupled; suboptimal	End-to-end optimized
Interpretability	Weak (via embeddings)	Better (via attention mechanisms)

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.119300	0.969382	0.800000	0.640000	0.800000	0.711111
2	0.782600	0.783712	0.800000	0.640000	0.800000	0.711111
3	0.890900	0.805560	0.800000	0.640000	0.800000	0.711111
4	0.823600	0.728813	0.800000	0.640000	0.800000	0.711111
5	0.796600	0.813704	0.776471	0.679641	0.776471	0.724306
6	0.680100	0.785266	0.729412	0.704342	0.729412	0.716431
7	0.436700	0.808377	0.788235	0.733333	0.788235	0.741348
8	0.477600	0.921163	0.776471	0.660000	0.776471	0.713514
9	0.344100	1.252109	0.705882	0.654418	0.705882	0.678915
10	0.287100	1.257946	0.800000	0.647619	0.800000	0.715789

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.439200	1.310259	0.800000	0.640000	0.800000	0.711111
2	0.759800	0.785657	0.800000	0.640000	0.800000	0.711111
3	0.869900	0.766365	0.800000	0.640000	0.800000	0.711111
4	0.866900	0.769237	0.800000	0.640000	0.800000	0.711111
5	0.851700	0.762193	0.800000	0.640000	0.800000	0.711111
6	0.734900	0.891209	0.694118	0.664789	0.694118	0.679137
7	0.553900	0.880783	0.682353	0.717647	0.682353	0.689412
8	0.553800	1.259274	0.682353	0.653521	0.682353	0.667626
9	0.495300	1.482099	0.623529	0.684706	0.623529	0.645343
10	0.224400	1.618874	0.658824	0.695343	0.658824	0.676078

	Unnamed: 0	Data	Countries	Local	Industry Sector	Accident Level	Potential Accident Level	Genre	Employee or Third Party	Critical Risk	Description
0	0	2016-01-01	Country_01	Local_01	Mining	I	IV	Male	Third Party	Pressed	While removing the drill rod of the Jumbo 08 f...
1	1	2016-01-02	Country_02	Local_02	Mining	I	IV	Male	Employee	Pressurized Systems	During the activation of a sodium sulphide pum...
2	2	2016-01-06	Country_01	Local_03	Mining	I	III	Male	Third Party (Remote)	Manual Tools	In the sub-station MILPO located at level +170...
3	3	2016-01-08	Country_01	Local_04	Mining	I	I	Male	Third Party	Others	Being 9:45 am. approximately in the Nv. 1880 C...
4	4	2016-01-10	Country_01	Local_04	Mining	IV	IV	Male	Third Party	Others	Approximately at 11:45 a.m. in circumstances t...

	Country	City	Industry Sector	Accident Level	Potential Accident Level	Gender	Employee type	Critical Risk	Description
count	418	418	418	418	418	418	418	418	418
unique	3	12	3	5	6	2	3	33	411
top	Country_01	Local_03	Mining	I	IV	Male	Third Party	Others	During the activity of chuteo of ore in hopper...
freq	248	89	237	309	141	396	185	229	2

	Date	Country	City	Industry Sector	Accident Level	Potential Accident Level	Gender	Employee type	Critical Risk	Description	Year	Month	Day	Weekday	Week of the Year	Season
420	2017-07-04	Country_01	Local_04	Mining	I	III	Male	Third Party	Others	Being approximately 5:00 a.m. approximately, w...	2017	7	4	1	27	Winter
421	2017-07-04	Country_01	Local_03	Mining	I	II	Female	Employee	Others	The collaborator moved from the infrastructure...	2017	7	4	1	27	Winter
422	2017-07-05	Country_02	Local_09	Metals	I	II	Male	Employee	Venomous Animals	During the environmental monitoring activity i...	2017	7	5	2	27	Winter
423	2017-07-06	Country_02	Local_05	Metals	I	II	Male	Employee	Cut	The Employee performed the activity of strippi...	2017	7	6	3	27	Winter
424	2017-07-09	Country_01	Local_04	Mining	I	II	Female	Third Party	Fall prevention (same level)	At 10:00 a.m., when the assistant cleaned the ...	2017	7	9	6	27	Winter

	Model	Train_Accuracy	Test_Accuracy	Train_Recall	Test_Recall	Train_Precision	Test_Precision	Train_f1_score	Test_f1_score
0	Naive_Bayes_smote	0.96	0.63	0.96	0.63	0.96	0.72	0.96	0.66
1	Naive_Bayes_tuned	0.99	0.70	0.99	0.70	0.99	0.63	0.99	0.66

	Model	Train_Accuracy	Test_Accuracy	Train_Recall	Test_Recall	Train_Precision	Test_Precision	Train_f1_score	Test_f1_score
0	Logistic_Regression_smote	0.99	0.7	0.99	0.7	0.99	0.66	0.99	0.67
1	Logistic_Regression_tuned	1.00	0.7	1.00	0.7	1.00	0.64	1.00	0.66

	Model	Train_Accuracy	Test_Accuracy	Train_Recall	Test_Recall	Train_Precision	Test_Precision	Train_f1_score	Test_f1_score
0	SVM_smote	1.0	0.74	1.0	0.74	1.0	0.55	1.0	0.63
1	SVM_tuned	1.0	0.74	1.0	0.74	1.0	0.55	1.0	0.63

	Model	Train_Accuracy	Test_Accuracy	Train_Recall	Test_Recall	Train_Precision	Test_Precision	Train_f1_score	Test_f1_score
0	Random_Forest_smote	1.00	0.74	1.00	0.74	1.00	0.56	1.00	0.64
1	Random_Forest_tuned	0.98	0.71	0.98	0.71	0.98	0.63	0.98	0.67

	Model	Train_Accuracy	Test_Accuracy	Train_Recall	Test_Recall	Train_Precision	Test_Precision	Train_f1_score	Test_f1_score
0	GradientBoost_smote	1.0	0.70	1.0	0.70	1.0	0.63	1.0	0.66
1	GradientBoost_tuned	1.0	0.73	1.0	0.73	1.0	0.64	1.0	0.68

	Model	Train_Accuracy	Test_Accuracy	Train_Recall	Test_Recall	Train_Precision	Test_Precision	Train_f1_score	Test_f1_score
0	Naive_Bayes	0.74	0.74	0.74	0.74	0.55	0.54	0.63	0.63
1	Logistic_Regression	0.74	0.74	0.74	0.74	0.55	0.54	0.63	0.63
2	SVM	0.77	0.74	0.77	0.74	0.81	0.54	0.70	0.63
3	Random_Forest	1.00	0.75	1.00	0.75	1.00	0.61	1.00	0.65
4	GradientBoost	1.00	0.69	1.00	0.69	1.00	0.57	1.00	0.62

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	0.954600	0.790300	0.800000	0.640000	0.800000	0.711111
2	0.672800	0.749937	0.800000	0.640000	0.800000	0.711111
3	0.776000	0.791157	0.764706	0.641975	0.764706	0.697987

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.358000	1.196756	0.800000	0.640000	0.800000	0.711111
2	0.783100	0.771782	0.800000	0.640000	0.800000	0.711111
3	0.889200	0.752803	0.800000	0.640000	0.800000	0.711111
4	0.809300	0.765685	0.800000	0.640000	0.800000	0.711111
5	0.717000	0.764758	0.800000	0.640000	0.800000	0.711111
6	0.685600	0.801644	0.788235	0.663617	0.788235	0.720490
7	0.404100	1.021075	0.717647	0.668493	0.717647	0.692199
8	0.413800	1.242371	0.600000	0.697150	0.600000	0.640632
9	0.256800	1.476683	0.564706	0.668908	0.564706	0.609235
10	0.194500	1.912631	0.517647	0.694342	0.517647	0.586964

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	0.956200	0.772401	0.800000	0.640000	0.800000	0.711111
2	0.713700	0.796836	0.800000	0.640000	0.800000	0.711111
3	0.898600	0.761021	0.800000	0.640000	0.800000	0.711111

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	0.949700	0.770693	0.800000	0.640000	0.800000	0.711111
2	0.683800	0.775398	0.800000	0.640000	0.800000	0.711111
3	0.835000	0.771040	0.800000	0.640000	0.800000	0.711111

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	0.955400	0.768249	0.800000	0.640000	0.800000	0.711111
2	0.722200	0.808175	0.800000	0.640000	0.800000	0.711111
3	0.804900	0.733474	0.800000	0.640000	0.800000	0.711111

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	0.973600	0.779485	0.800000	0.640000	0.800000	0.711111
2	0.726500	0.808522	0.800000	0.640000	0.800000	0.711111
3	0.855300	0.762096	0.800000	0.640000	0.800000	0.711111

	Model	Accuracy	Precision	Recall	F1
0	RoBERTa	0.800000	0.640000	0.800000	0.711111
1	XLNet	0.800000	0.640000	0.800000	0.711111
2	DistilBERT	0.800000	0.640000	0.800000	0.711111
3	ALBERT	0.800000	0.640000	0.800000	0.711111
4	BERT	0.764706	0.641975	0.764706	0.697987

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.056800	0.797825	0.800000	0.640000	0.800000	0.711111
2	0.724400	0.752655	0.800000	0.640000	0.800000	0.711111
3	0.840100	0.734160	0.800000	0.640000	0.800000	0.711111
4	0.725900	0.697344	0.800000	0.640000	0.800000	0.711111
5	0.728300	0.732013	0.788235	0.645783	0.788235	0.709934
6	0.591000	0.765854	0.764706	0.675019	0.764706	0.717067
7	0.501600	0.995020	0.800000	0.647619	0.800000	0.715789
8	0.454200	1.064920	0.752941	0.678140	0.752941	0.713314
9	0.463900	1.259467	0.800000	0.647619	0.800000	0.715789
10	0.299100	1.911444	0.564706	0.714751	0.564706	0.624193

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.150000	0.861627	0.800000	0.640000	0.800000	0.711111
2	0.744300	0.768885	0.800000	0.640000	0.800000	0.711111
3	0.873900	0.761136	0.800000	0.640000	0.800000	0.711111
4	0.821700	0.721361	0.800000	0.640000	0.800000	0.711111
5	0.592000	0.723359	0.788235	0.645783	0.788235	0.709934
6	0.723500	0.772278	0.752941	0.720157	0.752941	0.724295
7	0.480600	0.955723	0.800000	0.640000	0.800000	0.711111
8	0.599800	1.009586	0.658824	0.690596	0.658824	0.671569
9	0.860400	0.991177	0.623529	0.657703	0.623529	0.637018
10	0.392600	0.845117	0.717647	0.684370	0.717647	0.698176

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.444200	1.283723	0.729412	0.642590	0.729412	0.683187
2	0.861600	0.816862	0.800000	0.640000	0.800000	0.711111
3	0.897100	0.773485	0.800000	0.640000	0.800000	0.711111
4	0.800800	0.822465	0.800000	0.640000	0.800000	0.711111
5	0.685200	0.796234	0.764706	0.634146	0.764706	0.693333

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.457800	1.340572	0.800000	0.640000	0.800000	0.711111
2	0.714800	0.806190	0.800000	0.640000	0.800000	0.711111
3	0.881700	0.751741	0.800000	0.640000	0.800000	0.711111
4	0.822300	0.727247	0.800000	0.640000	0.800000	0.711111

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.420000	1.302629	0.800000	0.640000	0.800000	0.711111
2	0.815800	0.781738	0.800000	0.640000	0.800000	0.711111
3	0.890600	0.766387	0.800000	0.640000	0.800000	0.711111
4	0.828800	0.769921	0.800000	0.640000	0.800000	0.711111

AIML Capstone Project - Industrial safety¶

PROBLEM STATEMENT¶

Columns description:¶

Project Objective:¶

Project Task¶

Milestone 1:¶

Abstract:¶

Objective¶

Data:¶

Findings and Approach:¶

Abstract:¶

Objective¶

Data:¶

Findings and Approach:¶

Import Basic Libraries¶

Step 1: Import Dataset¶

Step 2: Data cleaning¶

EDA - Univariate Analysis¶

Adding more useful columns to help with data analysis, EDA and explore possible patterns¶

Creating Trend charts of the number on incidents across day, week, month, year and seasons¶

EDA using bi-variate and multi-variate analysis:¶

EDA Summary¶

Step 3: NLP Pre-processing¶

Step 4: Data preparation - Cleansed data in .xlsx or .csv file¶

Step 5: Design train and test basic machine learning classifiers¶

Deciding Suitable Models¶

Importing libraries for performing classification using ML models:¶

Classification using base models¶

Model performance Improvement by Balancing data using SMOTE and Hyper-parameter Tuning¶

1. Naive Bayes¶

2. Logistic regression¶

3. SVM¶

4. Random Forest¶

5. GradientBoost¶

Display and compare all the models using balanced data(SMOTE) and hyper-parameter tuned models with their train and test metrics.¶

Display and compare all the base models trained on unbalanced data with their train and test metrics.¶

Display and compare all the models with their train and test metrics.¶

Select the final best trained model¶

Insights and Conclusion:¶

Improvements:¶

Improvements Attempted¶

Scope of Improvement¶

Milestone 2:¶

Import Basic Libraries¶

First Approach: BERT Embeddings + Traditional Classifiers¶

Defining the tokenizer and get embeddings function¶

Get Embeddings on cleaned data and Label Encoding¶

Splitting data into train and test¶

Different classifiers on cleaned data's BERT embeddings¶

Insights:¶

Get Embeddings, Label Encoding on raw data and Create Train and Test Data¶

Different classifiers on raw data's BERT embeddings¶

Insights:¶

Limitations and why we need fine tuning:¶

1. Loss of Contextual Information¶

2. Inefficient Handling of Sequence-Level Features¶

3. Limited Adaptation to Task-Specific Data¶

4. Class Imbalance Challenges¶

5. Scalability and Computational Overheads¶

6. Lack of End-to-End Optimization¶

7. Difficulty in Capturing Rare and Subtle Patterns¶

8. Impact on Fine-Grained Classifications¶

9. Handling Ambiguity in Descriptions¶

10. Lack of Model Interpretability¶

Summary: Key Advantages of Fine-Tuning Over Embedding-Based Classification¶

Recommendation¶

Second Approach: Fine-Tuning Transformer Models for Sequence Classification¶

Using Models like BERT, Roberta, DistilBert, XLNet, Albert on Raw Data¶

Import Required Libraries¶

Load and Preprocess Data¶

Define Model Selection, Training Process, Run Each Model, Evaluate Performance and Compare Model Results.¶

Insights:¶

Code Analysis and Function¶

Summary:¶

Exploring ways to get train and test metrics and trying to get better metrics after increasing the epochs to 10 in the below step:¶

Insights:¶

Steps in Detail¶

Summary¶

Modified the above code to pick best model based on F1 score in the below steps:¶

Insights:¶

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.002400	0.766483	0.800000	0.640000	0.800000	0.711111
2	0.701100	0.763081	0.800000	0.640000	0.800000	0.711111
3	0.862500	0.757904	0.800000	0.640000	0.800000	0.711111
4	0.676300	0.714607	0.788235	0.638095	0.788235	0.705263

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.185000	0.974409	0.800000	0.640000	0.800000	0.711111
2	0.737400	0.760119	0.800000	0.640000	0.800000	0.711111
3	0.818800	0.777122	0.800000	0.640000	0.800000	0.711111
4	0.761500	0.840861	0.800000	0.640000	0.800000	0.711111

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.049600	1.351589	0.411765	0.532632	0.411765	0.448841
2	0.265800	1.235443	0.588235	0.605933	0.588235	0.590708
3	0.042300	1.663724	0.670588	0.541629	0.670588	0.599249
4	0.001900	2.107168	0.694118	0.546618	0.694118	0.611600
5	0.211200	2.259966	0.729412	0.547059	0.729412	0.625210
6	0.012700	2.161124	0.729412	0.553650	0.729412	0.629492
7	0.033800	2.203558	0.729412	0.553650	0.729412	0.629492
8	0.005400	2.285457	0.729412	0.553650	0.729412	0.629492
9	0.057300	2.331009	0.729412	0.553650	0.729412	0.629492

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	0.994800	1.322501	0.341176	0.632277	0.341176	0.409500
2	0.152200	2.263594	0.364706	0.563000	0.364706	0.422125
3	0.114200	2.062973	0.705882	0.577066	0.705882	0.631593
4	0.001900	2.463611	0.600000	0.584977	0.600000	0.592008
5	0.152900	2.354644	0.729412	0.553650	0.729412	0.629492
6	0.004900	2.478804	0.729412	0.586657	0.729412	0.641258
7	0.135400	2.564391	0.729412	0.586657	0.729412	0.641258
8	0.009500	2.641434	0.705882	0.570147	0.705882	0.627286
9	0.049600	2.637519	0.705882	0.570147	0.705882	0.627286

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.168700	1.407919	0.305882	0.620078	0.305882	0.329780
2	0.136600	1.395260	0.576471	0.578972	0.576471	0.577179
3	0.005100	2.059941	0.694118	0.546618	0.694118	0.611600
4	0.001900	2.491858	0.670588	0.555882	0.670588	0.607871
5	0.037000	2.584239	0.694118	0.553537	0.694118	0.615907
6	0.019300	2.640933	0.694118	0.553537	0.694118	0.615907
7	0.024000	2.694793	0.694118	0.553537	0.694118	0.615907
8	0.004800	2.730828	0.694118	0.553537	0.694118	0.615907

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	0.836000	1.111737	0.647059	0.614902	0.647059	0.629265
2	0.221800	1.628147	0.505882	0.605473	0.505882	0.546473
3	0.046000	2.495870	0.729412	0.553650	0.729412	0.629492
4	0.002900	4.445165	0.435294	0.640326	0.435294	0.499970
5	0.070200	2.492741	0.717647	0.565147	0.717647	0.632332
6	0.020000	2.966770	0.705882	0.590118	0.705882	0.642583
7	0.029800	2.890443	0.694118	0.575387	0.694118	0.629200
8	0.001900	3.000550	0.705882	0.542324	0.705882	0.613387
9	0.062600	2.985967	0.705882	0.549020	0.705882	0.617647

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	0.927300	1.255324	0.376471	0.551288	0.376471	0.440584
2	0.229400	1.119273	0.588235	0.561497	0.588235	0.574555
3	0.035100	1.588666	0.576471	0.631529	0.576471	0.591244
4	0.932200	1.172791	0.576471	0.564706	0.576471	0.568627
5	0.461700	1.790805	0.623529	0.549556	0.623529	0.583798
6	0.032300	2.113769	0.717647	0.551363	0.717647	0.623611
7	0.081500	2.533544	0.729412	0.547059	0.729412	0.625210
8	0.005200	2.596861	0.729412	0.547059	0.729412	0.625210
9	0.052000	2.654782	0.729412	0.547059	0.729412	0.625210
10	0.000300	2.662885	0.729412	0.547059	0.729412	0.625210

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	0.855400	0.743329	0.800000	0.640000	0.800000	0.711111
2	0.626700	0.626402	0.800000	0.640000	0.800000	0.711111
3	0.734900	0.622588	0.800000	0.640000	0.800000	0.711111
4	0.637000	0.589013	0.800000	0.640000	0.800000	0.711111
5	0.541500	0.606670	0.800000	0.681077	0.800000	0.727581
6	0.483200	0.707722	0.682353	0.670844	0.682353	0.676521
7	0.106100	0.851349	0.811765	0.697023	0.811765	0.747698
8	0.143000	1.069056	0.800000	0.724706	0.800000	0.748023
9	0.278100	1.557687	0.600000	0.684130	0.600000	0.636531
10	0.143500	1.821103	0.647059	0.688259	0.647059	0.666051

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.118200	1.047989	0.800000	0.640000	0.800000	0.711111
2	0.630900	0.653031	0.800000	0.640000	0.800000	0.711111
3	0.767500	0.606745	0.800000	0.640000	0.800000	0.711111
4	0.686900	0.580249	0.800000	0.640000	0.800000	0.711111
5	0.639300	0.627484	0.800000	0.710489	0.800000	0.729843
6	0.901200	0.657368	0.705882	0.680112	0.705882	0.692373
7	0.321200	0.814894	0.764706	0.701604	0.764706	0.726545
8	0.292000	1.048538	0.741176	0.718972	0.741176	0.725165
9	0.219300	1.454005	0.658824	0.727413	0.658824	0.681675
10	0.234600	1.804222	0.717647	0.739312	0.717647	0.725391

Epoch	Training Loss	Validation Loss	Accuracy	Precision	Recall	F1
1	1.014300	0.911074	0.800000	0.640000	0.800000	0.711111
2	0.649000	0.631885	0.800000	0.640000	0.800000	0.711111
3	0.714900	0.621069	0.800000	0.640000	0.800000	0.711111
4	0.632800	0.616022	0.800000	0.640000	0.800000	0.711111
5	0.593700	0.612611	0.788235	0.638095	0.788235	0.705263
6	0.511200	0.697658	0.752941	0.690092	0.752941	0.719589
7	0.280900	0.857517	0.800000	0.640000	0.800000	0.711111
8	0.185300	0.941398	0.729412	0.708906	0.729412	0.718146
9	0.293300	1.426194	0.611765	0.718460	0.611765	0.652975
10	0.102400	1.674249	0.635294	0.754812	0.635294	0.675424